Computes product of two sparse matrices stored in the CSR format (3-array variation) with one-based indexing (deprecated).
Exports CSR matrix from internal representation.
Computes the product of a sparse matrix and a dense matrix.
Computes two matrix-vector products using a general matrix (real data)
Intel® Math Kernel Library implements routines from the LAPACK package that are used for solving systems of linear equations, linear least squares problems, eigenvalue and singular value problems, and performing a number of related computational tasks. The library includes LAPACK routines for both real and complex data. Routines are supported for systems of equations with the following types of matrices:
Computes the Cholesky factorization of a symmetric (Hermitian) positive-definite matrix.
This section describes the LAPACK routines for solving systems of linear equations. Before calling most of these routines, you need to factorize the matrix of your system of equations (see Routines for Matrix Factorization). However, the factorization is not necessary if your system of equations has a triangular matrix.
Solves a system of linear equations with a UDU- or LDL-factored symmetric coefficient matrix.
Estimates the reciprocal of the condition number of a symmetric matrix.
Refines the solution of a system of linear equations with a symmetric (Hermitian) positive-definite coefficient matrix stored in a packed format and estimates its error.