Deflated block Krylov subspace methods for large scale eigenvalue problems

Qiang Niu*, Linzhang Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

We discuss a class of deflated block Krylov subspace methods for solving large scale matrix eigenvalue problems. The efficiency of an Arnoldi-type method is examined in computing partial or closely clustered eigenvalues of large matrices. As an improvement, we also propose a refined variant of the Arnoldi-type method. Comparisons show that the refined variant can further improve the Arnoldi-type method and both methods exhibit very regular convergence behavior.

Original languageEnglish
Pages (from-to)636-648
Number of pages13
JournalJournal of Computational and Applied Mathematics
Volume234
Issue number3
DOIs
Publication statusPublished - 1 Jun 2010
Externally publishedYes

Keywords

  • Arnoldi process
  • Krylov subspace
  • Refined approximate eigenvector
  • Ritz value
  • Ritz vector

Cite this