- Referenced in 1695 articles
- singular value problems. The associated matrix factorizations (LU, Cholesky, QR, SVD, Schur, generalized Schur ... computations such as reordering of the Schur factorizations and estimating condition numbers. Dense and banded ... reorganizing the algorithms to use block matrix operations, such as matrix multiplication, in the innermost...
- Referenced in 1485 articles
- functions for constructing test matrices, computing matrix factorizations, visualizing matrices, and carrying out direct search ... toolbox supersedes the author’s earlier Test Matrix Toolbox (final release 1995). The toolbox...
- Referenced in 841 articles
- problems. For many standard problems, a matrix factorization is not required. Only the action...
- Referenced in 417 articles
- iterative refinement, for LU and Cholesky factorization, matrix inversion, full-rank linear least squares problems ... orthogonal and generalized orthogonal factorizations, orthogonal transformation routines, reductions to upper Hessenberg, bidiagonal and tridiagonal ... solvers for LU, Cholesky, and QR, the matrix sign function for eigenproblems...
- Referenced in 412 articles
- ordering and analyzing a sparse matrix, computing the numerical factorization, solving a system with ... factors, transposing and permuting a sparse matrix, and converting between sparse matrix representations.\parThe simple ... from the details of the complex sparse factorization data structures by returning simple handles...
- Referenced in 502 articles
- unsymmetric matrices; Version for complex arithmetic; Parallel factorization and solve phases (uniprocessor version also available ... Iterative refinement and backward error analysis; Various matrix input formats assembled format; distributed ... assembled format; elemental format; Partial factorization and Schur complement matrix (centralized or 2D block-cyclic...
- Referenced in 458 articles
- package (for maintaining sparse LU factors of the basis matrix), automatic scaling of linear contraints...
- Referenced in 92 articles
- facilities, matrix modification, partial solution for matrix factors, solution of multiple right-hand sides...
- Referenced in 180 articles
- dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor...
- Referenced in 193 articles
- through forward and back substitution. The LU factorization routines can handle non-square matrices ... square matrices. The matrix columns may be preordered (before factorization) either through library or user...
- Referenced in 33 articles
- optimal gradient method for non-negative matrix factorization. Nonnegative matrix factorization (NMF) is a powerful ... product of two low-rank nonnegative matrix factors. It has been widely applied to signal ... particular, at each iteration round, the matrix factor is updated by using the PG method ... optimally at rate in optimizing each matrix factor, it is superior...
- Referenced in 46 articles
- particular they do not rely on matrix factorizations of the linearized constraints but use solutions...
- Referenced in 113 articles
- updating/downdating a sparse Cholesky factorization, solving linear systems, updating/downdating the solution to the triangular system ... sparse matrix functions for both symmetric and unsymmetric matrices. Its supernodal Cholesky factorization relies ... well as in several other sparse matrix functions...
- Referenced in 746 articles
- extending program syntax; analytic differentiation and integration; factorization of polynomials; facilities for the solution ... wide variety of special functions; Dirac matrix calculations of interest to high energy physicists...
- Referenced in 37 articles
- matrix completion, robust PCA, nonnegative matrix factorization, k-means, and many more. For more information...
- Referenced in 64 articles
- applying the usual ILU factorization to a matrix obtained from a multicolor ordering.par We present...
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- than the supernodal solvers when it factors a matrix completely, but it can drop small ... Core Sparse LU with Partial Pivoting Factor and Solve. Can solve huge unsymmetric linear systems ... dropping nonzeros from the coefficient matrix and them factoring the preconditioner directly. Recursive Vaidya ... nonzeros, but they don’t factor the resulting matrix completely. Instead, they eliminate rows...
- Referenced in 36 articles
- matrix completion, robust PCA, nonnegative matrix factorization, k-means, and many more...
- Referenced in 35 articles
- Meanwhile, the gray-level co-occurrence matrix factors extracted from the CA images are used...
- Referenced in 71 articles
- fusion: define your own (coupled) matrix and tensor factorizations with structured factors and support...