AbstractThe spectral decomposition of a symmetric matrix A with small off-diagonal and distinct diagonal elements can be approximated using a direct scheme of R. Davies and Modi (Linear Algebra Appl. 77 (1986) 61). In this paper a generalization of this method for computing the singular value decomposition of close-to-diagonal A∈Rm×n is presented. When A has repeated or “close” singular values it is possible to apply the direct method to split the problem in two with one part containing the well-separated singular values and one requiring the computation of the “close” singular values
AbstractIn this note we consider an iterative algorithm for moving a triangular matrix toward diagon...
AbstractA Jacobi-type updating algorithm for the SVD or the URV decomposition is developed, which is...
AbstractThis paper gives SVD perturbation bounds and expansions that are of use when an m×n, m⩾n mat...
AbstractThe spectral decomposition of a symmetric matrix A with small off-diagonal and distinct diag...
AbstractWe analyze when it is possible to compute the singular values and singular vectors of a matr...
In this paper we present an efficient method for updating the singular value decomposition (SVD) sub...
AbstractThe Partial Singular Value Decomposition (PSVD) subroutine computes a basis of the left and/...
The paper considers the singular value decomposition (SVD) of a general matrix. Some immediate appli...
AbstractComputing the singular values and vectors of a matrix is a crucial kernel in numerous scient...
AbstractGiven an m×n matrix A and a positive integer k, we describe a randomized procedure for the a...
AbstractIn this paper, an improved algorithm PSVD for computing the singular subspace of a matrix co...
The FEAST eigensolver is extended to the computation of the singular triplets of a large matrix $A$ ...
Jacobi-type iterative algorithms for the eigenvalue decomposition, singular value decomposition, and...
The singular value decomposition (SVD) is a basic tool for analyzing matrices. Regarding a general m...
The linear least squares problem of minimizing ||Ax~ - b~||_(2) where A is an m X n matrix, m >= ...
AbstractIn this note we consider an iterative algorithm for moving a triangular matrix toward diagon...
AbstractA Jacobi-type updating algorithm for the SVD or the URV decomposition is developed, which is...
AbstractThis paper gives SVD perturbation bounds and expansions that are of use when an m×n, m⩾n mat...
AbstractThe spectral decomposition of a symmetric matrix A with small off-diagonal and distinct diag...
AbstractWe analyze when it is possible to compute the singular values and singular vectors of a matr...
In this paper we present an efficient method for updating the singular value decomposition (SVD) sub...
AbstractThe Partial Singular Value Decomposition (PSVD) subroutine computes a basis of the left and/...
The paper considers the singular value decomposition (SVD) of a general matrix. Some immediate appli...
AbstractComputing the singular values and vectors of a matrix is a crucial kernel in numerous scient...
AbstractGiven an m×n matrix A and a positive integer k, we describe a randomized procedure for the a...
AbstractIn this paper, an improved algorithm PSVD for computing the singular subspace of a matrix co...
The FEAST eigensolver is extended to the computation of the singular triplets of a large matrix $A$ ...
Jacobi-type iterative algorithms for the eigenvalue decomposition, singular value decomposition, and...
The singular value decomposition (SVD) is a basic tool for analyzing matrices. Regarding a general m...
The linear least squares problem of minimizing ||Ax~ - b~||_(2) where A is an m X n matrix, m >= ...
AbstractIn this note we consider an iterative algorithm for moving a triangular matrix toward diagon...
AbstractA Jacobi-type updating algorithm for the SVD or the URV decomposition is developed, which is...
AbstractThis paper gives SVD perturbation bounds and expansions that are of use when an m×n, m⩾n mat...