Update dependency com_gitlab_libeigen_eigen to v5

[mend-for-github.amrom.workers.dev]

This PR contains the following updates:

Package	Type	Update	Change
com_gitlab_libeigen_eigen	http_archive	major	3.3.8 -> 5.0.1

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Release Notes

libeigen/eigen (com_gitlab_libeigen_eigen)

v5.0.1

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A few bug-fixes from the master branch, including

• Dirty git state [#​2995]
• Failing geo_homogeneous tests [#​2977]
• Alignment issues [#​2982, #​2984]
• Missing C++20 <version> header [#​2986]
• BLAS/LAPACK build on windows [#​2980]

See the full lists of addressed bugs and merge requests for more details.

v5.0.0

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Eigen 5.0 provides many new features, performance enhancements, and bugfixes throughout Eigen’s core template expression infrastructure and linear algebra facilities. The full set of changes and related issues are too large to list here, but can be accessed via the release milestone %"5.0".

This is the last major release to support the C++14 language standard. The master branch and subsequent releases will require support for C++17.

Versioning

This release marks a transition to Semantic Versioning. Previously, Eigen used a WORLD.MAJOR.MINOR scheme. From now on, version numbers will follow the MAJOR.MINOR.PATCH format, indicating breaking changes, new features, and bug fixes, respectively. The WORLD version will remain 3 for this and subsequent releases for posterity. See the table below:

╔═════════╦═════╦═════╗
║ Release ║ 3.4 ║ 5.0 ║
╠═════════╬═════╬═════╣
║ WORLD   ║  3  ║  3  ║
║ MAJOR   ║  4  ║  5  ║
║ MINOR   ║  0  ║  0  ║
║ PATCH   ║  -  ║  0  ║
╚═════════╩═════╩═════╝

Breaking changes

• Eigen 5.X.X requires C++14. When building with GNU-compatible compilers, set -std=c++14 or later. As part of this change, some macros such as EIGEN_HAS_CXX11 have also been removed.
• The CMake build system has been modernized and older properties have been removed - projects relying on CMake may need to update their configurations [!485].
• All LGPL-licensed code has been removed (i.e. Constrained Conjugate Gradient) [!1197]. These were "unsupported" anyways, and weren't widely used.
• Due to name conflicts with other projects, Eigen::all and Eigen::last have been moved to Eigen::placeholders::all and Eigen::placeholders::last [!649].
• Any direct inclusion of an internal header (i.e. under a ../src/.. path) will result in a compilation error [!631].
• Runtime SVD options for computing thin/full U/V have been deprecated: use compile-time options instead [!826].
• Scalar (i.e. non-vectorized) comparisons now return masks with values of Scalar(1) rather than having all bits set to avoid undefined behavior [!1862].
• BLAS return types have been changed for Eigen BLAS to void instead of int for compatibility with other BLAS implementations [!1497].
• Eigen::aligned_allocator no longer inherits from std::allocator due to a change in the standard and the use of allocate_at_least [!1795].
• Euler angles are now returned in a more canonical form, potentially resulting in a change of behavior [!1301, !1314].
• Eigen's random number generation has changed, resulting in a change of behavior. Please do not rely on specific random numbers from Eigen - these were never guaranteed to be consistent across Eigen versions, nor are they generally consistent across platforms [!1437].

v3.4.1

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Many bug fixes have been backported from the main branch.

A list of new issues addressed can be found via the 3.4.1 label on GitLab.

Check the git commit history for the full list of changes.

v3.4.0

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Notice: 3.4.x will be the last major release series of Eigen that will support c++03.

Breaking changes

• Using float or double for indexing matrices, vectors and arrays will now fail to compile
• Behavioral change: Transform::computeRotationScaling() and Transform::computeScalingRotation() are now more continuous across degeneracies (see !349).

New features

• Add c++11 initializer_list constructors to Matrix and Array [doc]
• Add STL-compatible iterators for dense expressions [doc].
• New versatile API for sub-matrices, slices, and indexed views [doc].
• Add C++11 template aliases for Matrix, Vector, and Array of common sizes, including generic Vector<Type,Size> and RowVector<Type,Size> aliases [doc].
• New support for bfloat16.

New backends

• Arm SVE: fixed-length Scalable Vector Extensions vectors for uint32_t and float are available.
• MIPS MSA:: MIPS SIMD Architecture (MSA)
• AMD ROCm/HIP: generic GPU backend that unifies support for NVIDIA/CUDA and AMD/HIP.
• Power 10 MMA: initial support for Power 10 matrix multiplication assist instructions for float32 and float64, real and complex.

Improvements

• Eigen now uses the c++11 alignas keyword for static alignment. Users targeting C++17 only and recent compilers (e.g., GCC>=7, clang>=5, MSVC>=19.12) will thus be able to completely forget about all issues related to static alignment, including EIGEN_MAKE_ALIGNED_OPERATOR_NEW.
• Various performance improvements for products and Eigen's GEBP and GEMV kernels have been implemented:
  • By using half and quater-packets the performance of matrix multiplications of small to medium sized matrices has been improved
  • Eigen's GEMM now falls back to GEMV if it detects that a matrix is a run-time vector
  • The performance of matrix products using Arm Neon has been drastically improved (up to 20%)
  • Performance of many special cases of matrix products has been improved
• Large speed up from blocked algorithm for transposeInPlace.
• Speed up misc. operations by propagating compile-time sizes (col/row-wise reverse, PartialPivLU, and others)
• Faster specialized SIMD kernels for small fixed-size inverse, LU decomposition, and determinant.
• Improved or added vectorization of partial or slice reductions along the outer-dimension, for instance: colmajor_mat.rowwise().mean().

Elementwise math functions

• Many functions are now implemented and vectorized in generic (backend-agnostic) form.
• Many improvements to correctness, accuracy, and compatibility with c++ standard library.
  • Much improved implementation of ldexp.
  • Misc. fixes for corner cases, NaN/Inf inputs and singular points of many functions.
  • New implementation of the Payne-Hanek for argument reduction algorithm for sin and cos with huge arguments.
  • New faithfully rounded algorithm for pow(x,y).
• Speedups from (new or improved) vectorized versions of pow, log, sin, cos, arg, pow, log2, complex sqrt, erf, expm1, logp1, logistic, rint, gamma and bessel functions, and more.
• Improved special function support (Bessel and gamma functions, ndtri, erfc, inverse hyperbolic functions and more)
• New elementwise functions for absolute_difference, rint.

Dense matrix decompositions and solvers

• All dense linear solvers (i.e., Cholesky, *LU, *QR, CompleteOrthogonalDecomposition, *SVD) now inherit SolverBase and thus support .transpose(), .adjoint() and .solve() APIs.
• SVD implementations now have an info() method for checking convergence.
• Most decompositions now fail quickly when invalid inputs are detected.
• Optimized the product of a HouseholderSequence with the identity, as well as the evaluation of a HouseholderSequence to a dense matrix using faster blocked product.
• Fixed aliasing issues with in-place small matrix inversions.
• Fixed several edge-cases with empty or zero inputs.

Sparse matrix support, decompositions and solvers

• Enabled assignment and addition with diagonal matrix expressions.
• Support added for SuiteSparse KLU routines via the KLUSupport module. SuiteSparse must be installed to use this module.
• SparseCholesky now works with row-major matrices.
• Various bug fixes and performance improvements.

Type support

• Improved support for half
  • Native support added for ARM __fp16, CUDA/HIP __half, and F16C conversion intrinsics.
  • Better vectorization support added across all backends.
• Improved bool support
  • Partial vectorization support added for boolean operations.
  • Significantly improved performance (x25) for logical operations with Matrix or Tensor of bool.
• Improved support for custom types
  • More custom types work out-of-the-box (see #​2201).

Backend-specific improvements

• Arm NEON
  • Now provides vectorization for uint64_t, int64_t, uint32_t, int16_t, uint16_t, int16_t, int8_t, and uint8_t
  • Emulates bfloat16 support when using Eigen::bfloat16
  • Supports emulated and native float16 when using Eigen::half
• SSE/AVX/AVX512
  • General performance improvements and bugfixes.
  • Enabled AVX512 instructions by default if available.
  • New std::complex, half, and bfloat16 vectorization support added.
  • Many missing packet functions added.
• Altivec/Power
  • General performance improvement and bugfixes.
  • Enhanced vectorization of real and complex scalars.
  • Changes to the gebp_kernel specific to Altivec, using VSX implementation of the MMA instructions that gain speed improvements up to 4x for matrix-matrix products.
  • Dynamic dispatch for GCC greater than 10 enabling selection of MMA or VSX instructions based on __builtin_cpu_supports.
• GPU (CUDA and HIP)
  • Several optimized math functions added, better support for std::complex.
  • Added option to disable CUDA entirely by defining EIGEN_NO_CUDA.
  • Many more functions can now be used in device code (e.g. comparisons, small matrix inversion).
• ZVector
  • Vectorized float and std::complex<float> support added.
  • Added z14 support.
• SYCL
  • Redesigned SYCL implementation for use with the [https://eigen.tuxfamily.org/dox/unsupported/eigen_tensors.html Tensor] module, which can be enabled by defining EIGEN_USE_SYCL.
  • New generic memory model introduced used by TensorDeviceSycl.
  • Better integration with OpenCL devices.
  • Added many math function specializations.

Miscellaneous API Changes

• New setConstant(...) methods for preserving one dimension of a matrix by passing in NoChange.
• Added setUnit(Index i) for vectors that sets the ''i'' th coefficient to one and all others to zero.
• Added transpose(), adjoint(), conjugate() methods to SelfAdjointView.
• Added shiftLeft<N>() and shiftRight<N>() coefficient-wise arithmetic shift functions to Arrays.
• Enabled adding and subtracting of diagonal expressions.
• Allow user-defined default cache sizes via defining EIGEN_DEFAULT_L1_CACHE_SIZE, ..., EIGEN_DEFAULT_L3_CACHE_SIZE.
• Added EIGEN_ALIGNOF(X) macro for determining alignment of a provided variable.
• Allow plugins for VectorwiseOp by defining a file EIGEN_VECTORWISEOP_PLUGIN (e.g. -DEIGEN_VECTORWISEOP_PLUGIN=my_vectorwise_op_plugins.h).
• Allow disabling of IO operations by defining EIGEN_NO_IO.

Improvement to NaN propagation

• Improvements to NaN correctness for elementwise functions.
• New NaNPropagation template argument to control whether NaNs are propagated or suppressed in elementwise min/max and corresponding reductions on Array, Matrix, and Tensor.

New low-latency non-blocking ThreadPool module

• Originally a part of the Tensor module, Eigen::ThreadPool is now separate and more portable, and forms the basis for multi-threading in TensorFlow, for example.

Changes to Tensor module

• Support for c++03 was officially dropped in Tensor module, since most of the code was written in c++11 anyway. This will prevent building the code for CUDA with older version of nvcc.
• Performance optimizations of Tensor contraction
  • Speed up "outer-product-like" operations by parallelizing over the contraction dimension, using thread_local buffers and recursive work splitting.
  • Improved threading heuristics.
  • Support for fusing element-wise operations into contraction during evaluation.
• Performance optimizations of other Tensor operator
  • Speedups from improved vectorization, block evaluation, and multi-threading for most operators.
  • Significant speedup to broadcasting.
  • Reduction of index computation overhead, e.g. using fast divisors in TensorGenerator, squeezing dimensions in TensorPadding.
• Complete rewrite of the block (tiling) evaluation framework for tensor expressions lead to significant speedups and reduced number of memory allocations.
• Added new API for asynchronous evaluation of tensor expressions.
• Misc. minor behavior changes & fixes:
  • Fix const correctness for TensorMap.
  • Modify tensor argmin/argmax to always return first occurrence.
  • More numerically stable tree reduction.
  • Improve randomness of the tensor random generator.
  • Update the padding computation for PADDING_SAME to be consistent with TensorFlow.
  • Support static dimensions (aka IndexList) in resizing/reshape/broadcast.
  • Improved accuracy of Tensor FFT.

Changes to sparse iterative solvers

• Added new IDRS iterative linear solver.

Other relevant changes

• Eigen now provides an option to test with an external BLAS library

See the announcement for more details.

v3.3.9

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Changes since 3.3.8:

• Commit 4e5385c: Introduce rendering Doxygen math formulas with MathJax and the option EIGEN_DOC_USE_MATHJAX to control this.
• #​1746: Removed implementation of standard copy-constructor and standard copy-assign-operator from PermutationMatrix and Transpositions to allow malloc-less std::move.
• #​2036: Make sure the find_standard_math_library_test_program compiles and doesn't optimize away functions we try to test for.
• #​2046: Rename test/array.cpp to test/array_cwise.cpp to fix an issue with the C++ standard library header "array"
• #​2040: Fix an issue in test/ctorleak that occured when disabling exceptions.
• #​2011: Remove error counting in OpenMP parallel section in Eigen's GEMM parallelizing logic.
• #​2012: Define coeff-wise binary array operators for base class to fix an issue when using Eigen with C++20
• Commit bfdd4a9: Fix an issue with Intel® MKL PARDISO support.

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