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  2. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    Also known as: UTV decomposition, ULV decomposition, URV decomposition. Applicable to: m-by-n matrix A. Decomposition: =, where T is a triangular matrix, and U and V are unitary matrices. Comment: Similar to the singular value decomposition and to the Schur decomposition.

  3. Comparison of linear algebra libraries - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_linear...

    C++ template library; binds to optimized BLAS such as the Intel MKL; Includes matrix decompositions, non-linear solvers, and machine learning tooling Eigen: Benoît Jacob C++ 2008 3.4.0 / 08.2021 Free MPL2: Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms. Fastor [5]

  4. JAMA (numerical linear algebra library) - Wikipedia

    en.wikipedia.org/wiki/JAMA_(numerical_linear...

    LU decomposition; Singular value decomposition; QR decomposition; Cholesky decomposition; Versions exist for both C++ and the Java programming language. The C++ version uses the Template Numerical Toolkit for lower-level operations. The Java version provides the lower-level operations itself.

  5. Cholesky decomposition - Wikipedia

    en.wikipedia.org/wiki/Cholesky_decomposition

    In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced / ʃ ə ˈ l ɛ s k i / shə-LES-kee) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations.

  6. FETI-DP - Wikipedia

    en.wikipedia.org/wiki/FETI-DP

    The FETI-DP method is a domain decomposition method [1] that enforces equality of the solution at subdomain interfaces by Lagrange multipliers except at subdomain corners, which remain primal variables. The first mathematical analysis of the method was provided by Mandel and Tezaur. [2]

  7. Generalized singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Generalized_singular_value...

    In linear algebra, the generalized singular value decomposition (GSVD) is the name of two different techniques based on the singular value decomposition (SVD).The two versions differ because one version decomposes two matrices (somewhat like the higher-order or tensor SVD) and the other version uses a set of constraints imposed on the left and right singular vectors of a single-matrix SVD.

  8. Sparse matrix - Wikipedia

    en.wikipedia.org/wiki/Sparse_matrix

    PETSc, a large C library, containing many different matrix solvers for a variety of matrix storage formats. Trilinos, a large C++ library, with sub-libraries dedicated to the storage of dense and sparse matrices and solution of corresponding linear systems. Eigen3 is a C++ library that

  9. Tensor software - Wikipedia

    en.wikipedia.org/wiki/Tensor_software

    Xerus [52] is a C++ tensor algebra library for tensors of arbitrary dimensions and tensor decomposition into general tensor networks (focusing on matrix product states). It offers Einstein notation like syntax and optimizes the contraction order of any network of tensors at runtime so that dimensions need not be fixed at compile-time.

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    matrix decomposition techniques in c++ 5 download