The problem of computing a few of the largest or smallest singular values and associated singular vectors of a large matrix arises in many applications. This paper describes restarted block Lanczos bidiagonalization methods based on augmentation of Ritz vectors or harmonic Ritz vectors by block Krylov subspaces. © Springer Science+Business Media B.V. 2006
Reliable estimates for the condition number of a large, sparse, real matrix A are important in many ...
Reliable estimates for the condition number of a large, sparse, real matrix $A$ are important in man...
AbstractIn this paper we show how to improve the approximate solution of the large Sylvester equatio...
In this paper, we propose an implicitly restarted block Lanczos bidiagonalization (IRBLB) method for...
In this paper, we propose an implicitly restarted block Lanczos bidiagonalization (IRBLB) method for...
Abstract. The harmonic Lanczos bidiagonalization method can be used to compute the smallest singular...
We compare the block-Lanczos and the Davidson methods for computing a basis of a singular subspace a...
AbstractWe compare the block Lanczos and the Davidson methods for computing a basis of a singular su...
We compare the block-Lanczos and the Davidson methods for computing a basis of a singular subspace a...
Dedicated to Richard Varga on the occasion of his 70th birthday The Lanczos method can be generalize...
: We compare the block-Lanczos and the Davidson methods for computing a basis of a singular subspace...
Reliable estimates for the condition number of a large, sparse, real matrix A are important in many ...
Low rank approximation of large and/or sparse rectangular matrices is a very import ant topic in man...
The computation of the partial generalized singular value decomposition (GSVD) of large-scale matrix...
Reliable estimates for the condition number of a large, sparse, real matrix $A$ are important in man...
Reliable estimates for the condition number of a large, sparse, real matrix A are important in many ...
Reliable estimates for the condition number of a large, sparse, real matrix $A$ are important in man...
AbstractIn this paper we show how to improve the approximate solution of the large Sylvester equatio...
In this paper, we propose an implicitly restarted block Lanczos bidiagonalization (IRBLB) method for...
In this paper, we propose an implicitly restarted block Lanczos bidiagonalization (IRBLB) method for...
Abstract. The harmonic Lanczos bidiagonalization method can be used to compute the smallest singular...
We compare the block-Lanczos and the Davidson methods for computing a basis of a singular subspace a...
AbstractWe compare the block Lanczos and the Davidson methods for computing a basis of a singular su...
We compare the block-Lanczos and the Davidson methods for computing a basis of a singular subspace a...
Dedicated to Richard Varga on the occasion of his 70th birthday The Lanczos method can be generalize...
: We compare the block-Lanczos and the Davidson methods for computing a basis of a singular subspace...
Reliable estimates for the condition number of a large, sparse, real matrix A are important in many ...
Low rank approximation of large and/or sparse rectangular matrices is a very import ant topic in man...
The computation of the partial generalized singular value decomposition (GSVD) of large-scale matrix...
Reliable estimates for the condition number of a large, sparse, real matrix $A$ are important in man...
Reliable estimates for the condition number of a large, sparse, real matrix A are important in many ...
Reliable estimates for the condition number of a large, sparse, real matrix $A$ are important in man...
AbstractIn this paper we show how to improve the approximate solution of the large Sylvester equatio...
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