A Thick-Restart Lanczos Algorithm with Polynomial Filtering for Hermitian Eigenvalue Problems
Journal Article
·
· SIAM Journal on Scientific Computing
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Univ. of Minnesota, Twin Cities, MN (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Polynomial filtering can provide a highly effective means of computing all eigenvalues of a real symmetric (or complex Hermitian) matrix that are located in a given interval, anywhere in the spectrum. This paper describes a technique for tackling this problem by combining a thick-restart version of the Lanczos algorithm with deflation ("locking'') and a new type of polynomial filter obtained from a least-squares technique. Furthermore, the resulting algorithm can be utilized in a “spectrum-slicing” approach whereby a very large number of eigenvalues and associated eigenvectors of the matrix are computed by extracting eigenpairs located in different subintervals independently from one another.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC52-07NA27344
- OSTI ID:
- 1438696
- Report Number(s):
- LLNL-JRNL-685856
- Journal Information:
- SIAM Journal on Scientific Computing, Vol. 38, Issue 4; ISSN 1064-8275
- Publisher:
- SIAMCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 32 works
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Web of Science
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