Preconditioned iterations to calculate extreme eigenvalues
Conference
·
OSTI ID:223844
- Institut fuer Angewandte Mathematik, Leoben (Austria)
Common iterative algorithms to calculate a few extreme eigenvalues of a large, sparse matrix are Lanczos methods or power iterations. They converge at a rate proportional to the separation of the extreme eigenvalues from the rest of the spectrum. Appropriate preconditioning improves the separation of the eigenvalues. Davidson`s method and its generalizations exploit this fact. The authors examine a preconditioned iteration that resembles a truncated version of Davidson`s method with a different preconditioning strategy.
- Research Organization:
- Front Range Scientific Computations, Inc., Boulder, CO (United States); USDOE, Washington, DC (United States); National Science Foundation, Washington, DC (United States)
- OSTI ID:
- 223844
- Report Number(s):
- CONF-9404305--Vol.1; ON: DE96005735
- Country of Publication:
- United States
- Language:
- English
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