Search results
Results From The WOW.Com Content Network
SciPy provides support for several sparse matrix formats, linear algebra, and solvers. ALGLIB is a C++ and C# library with sparse linear algebra support; ARPACK Fortran 77 library for sparse matrix diagonalization and manipulation, using the Arnoldi algorithm; SLEPc Library for solution of large scale linear systems and sparse matrices
uBLAS is a C++ template class library that provides BLAS level 1, 2, 3 functionality for dense, packed and sparse matrices. Dlib: Davis E. King C++ 2006 19.24.2 / 05.2023 Free Boost C++ template library; binds to optimized BLAS such as the Intel MKL; Includes matrix decompositions, non-linear solvers, and machine learning tooling Eigen: Benoît ...
Hermes Project: C++/Python library for rapid prototyping of space- and space-time adaptive hp-FEM solvers. IML++ is a C++ library for solving linear systems of equations, capable of dealing with dense, sparse, and distributed matrices. IT++ is a C++ library for linear algebra (matrices and vectors), signal processing and communications ...
Dense and sparse matrices are supported. [2] Various matrix decompositions are provided through optional integration with Linear Algebra PACKage , Automatically Tuned Linear Algebra Software (ATLAS), and ARPACK. [3] High-performance BLAS/LAPACK replacement libraries such as OpenBLAS and Intel MKL can also be used.
The Matrix Template Library version 4 is a generic C++ template library providing sparse and dense BLAS functionality. MTL4 establishes an intuitive interface (similar to MATLAB ) and broad applicability thanks to generic programming .
LDPC codes functionally are defined by a sparse parity-check matrix. This sparse matrix is often randomly generated, subject to the sparsity constraints—LDPC code construction is discussed later. These codes were first designed by Robert Gallager in 1960. [5] Below is a graph fragment of an example LDPC code using Forney's factor graph notation.
A common choice is to use the sparsity pattern of A 2 instead of A; this matrix is appreciably more dense than A, but still sparse over all. This preconditioner is called ILU(1). One can then generalize this procedure; the ILU(k) preconditioner of a matrix A is the incomplete LU factorization with the sparsity pattern of the matrix A k+1.
In Mathematica, the function "CholeskyDecomposition" can be applied to a matrix. In C++, multiple linear algebra libraries support this decomposition: The Armadillo (C++ library) supplies the command chol to perform Cholesky decomposition. The Eigen library supplies Cholesky factorizations for both sparse and dense matrices.