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
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
AbstractThe solution of the linear matrix equations (i) AXB+CYD=E and (ii) (AXB, FXG)=(E, H) are con...
The generalized singular value decomposition (GSVD) of a pair of matrices expresses each matrix as a...
AbstractThe spectral decomposition of a symmetric matrix A with small off-diagonal and distinct diag...
In this paper we present an efficient method for updating the singular value decomposition (SVD) sub...
In this paper we derive a new algorithm for constructing a unitary decomposition of a sequence of ma...
In this paper we derive a new algorithm for constructing a unitary decomposition of a sequence of ma...
The linear least squares problem of minimizing ||Ax~ - b~||_(2) where A is an m X n matrix, m >= ...
A method is studied for computation of the spectral decomposition of a symmetric matrix with high pr...
Let A be an m x n matrix with m greater than or equal to n. Then one form of the singular-value deco...
The Singular Value Decomposition (SVD) is very well known. We provide an intuitive proof for real ma...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental m...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
Conventional algorithms for the (symmetric or non-symmetric) eigenvalue decomposition and the singul...
We shall consider a form of matrix factorization known as singular value decomposition (SVD) that is...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
AbstractThe solution of the linear matrix equations (i) AXB+CYD=E and (ii) (AXB, FXG)=(E, H) are con...
The generalized singular value decomposition (GSVD) of a pair of matrices expresses each matrix as a...
AbstractThe spectral decomposition of a symmetric matrix A with small off-diagonal and distinct diag...
In this paper we present an efficient method for updating the singular value decomposition (SVD) sub...
In this paper we derive a new algorithm for constructing a unitary decomposition of a sequence of ma...
In this paper we derive a new algorithm for constructing a unitary decomposition of a sequence of ma...
The linear least squares problem of minimizing ||Ax~ - b~||_(2) where A is an m X n matrix, m >= ...
A method is studied for computation of the spectral decomposition of a symmetric matrix with high pr...
Let A be an m x n matrix with m greater than or equal to n. Then one form of the singular-value deco...
The Singular Value Decomposition (SVD) is very well known. We provide an intuitive proof for real ma...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental m...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
Conventional algorithms for the (symmetric or non-symmetric) eigenvalue decomposition and the singul...
We shall consider a form of matrix factorization known as singular value decomposition (SVD) that is...
The symmetric eigenvalue decomposition and the singular value decomposition (SVD) are fundamental ma...
AbstractThe solution of the linear matrix equations (i) AXB+CYD=E and (ii) (AXB, FXG)=(E, H) are con...
The generalized singular value decomposition (GSVD) of a pair of matrices expresses each matrix as a...