Subspace iterative methods for eigenvalue problems
โ Scribed by T. Zhang; G.H. Golub; K. H. Law
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 169 KB
- Volume
- 294
- Category
- Article
- ISSN
- 0024-3795
No coin nor oath required. For personal study only.
โฆ Synopsis
This paper presents novel perturbation bounds for generalized symmetric positive deยฎnite eigenvalue problems. The bounds provide the insights for an observed computational phenomenon that is not easily explained by the existing bounds developed previously. Using the new bounds, we provide an analysis of a subspace Newton type procedure for computing a few extreme eigenpairs for generalized symmetric positive deยฎnite systems. A preconditioned version of this subspace iterative method is also studied.
๐ SIMILAR VOLUMES
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 A
We present two iterative KAM methods for eigenvalue problems[ We discuss their convergence properties for matrices of \_nite dimension when a perturbation parameter e is varied[ We observe di}erent domains separated by Julia sets related to avoided crossings[ ร 0887 Elsevier Science Ltd[ All rights