Computational experience with sequential and parallel, preconditioned Jacobi--Davidson for large, sparse symmetric matrices
type: Item
instance of: scholarly article
title: Computational experience with sequential and parallel, preconditioned Jacobi--Davidson for large, sparse symmetric matrices
zbMATH Open document ID: 1022.65037
DOI: 10.1016/S0021-9991(03)00190-6
author: Luca Bergamaschi
author: Giorgio Pini
author: Flavio Sartoretto
published in: Journal of Computational Physics
publication date: 2003-07-30T00:00:00Z
Mathematics Subject Classification ID: 65F15
Mathematics Subject Classification ID: 65F35
Mathematics Subject Classification ID: 65Y05
Mathematics Subject Classification ID: 65F50
zbMATH DE Number: 1957083
zbMATH Keywords: eigenvalues
zbMATH Keywords: sparse approximate inverses
zbMATH Keywords: parallel algorithms
zbMATH Keywords: Jacobi-Davidson method
zbMATH Keywords: paralell algorithms
zbMATH Keywords: preconditioning
zbMATH Keywords: large, sparse matrices
zbMATH Keywords: approximate inverse preconditioners
zbMATH Keywords: performance
zbMATH Keywords: convergence
describes a project that uses: JDQR
describes a project that uses: JDQZ
describes a project that uses: ARPACK
describes a project that uses: ParaSails
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 1957083