A refined subspace iteration algorithm for large sparse eigenproblems
type: Item
instance of: scholarly article
title: A refined subspace iteration algorithm for large sparse eigenproblems
zbMATH Open document ID: 0939.65057
DOI: 10.1016/S0168-9274(99)00008-2
author: Zhongxiao Jia
published in: Applied Numerical Mathematics
publication date: 2000-06-19T00:00:00Z
review text: Two innovations to subspace iteration are presented. Such iterations, which are extensions of the power method, are very popular for computing a few dominant eigenvalues and eigenvectors of large sparse matrices. It is shown that the refined algorithms derived from the proposed innovations are significantly better than conventional algorithms if the dimension of the subspace is larger than the number of desired eigenpairs. Three numerical examples are given and the relative efficiency of the proposed algorithms is shown as a result of a set of computational experiments.
Mathematics Subject Classification ID: 65F15
Mathematics Subject Classification ID: 65F50
zbMATH DE Number: 1399548
zbMATH Keywords: large sparse matrices
zbMATH Keywords: eigenvalues
zbMATH Keywords: subspace iteration
zbMATH Keywords: power method
zbMATH Keywords: eigenvectors
zbMATH Keywords: refined algorithms
zbMATH Keywords: numerical examples
reviewed by: R. P. Tewarson
describes a project that uses: ARPACK
MaRDI profile type: Publication
full work available at URL: s0168-9274(99)00008-2
OpenAlex ID: W2030827135
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description: scientific article; zbMATH DE number 1399548