An implicitly restarted bidiagonal Lanczos Method forLarge-scale singular value problems
Low rank approximation of large and/or sparse rectangular matrices is a very import ant topic in many application problems and is closely related to the sin- gular value decomposition of the matrices. In this paper, we propose an implicit restart scheme for the bidiagonal Lanczos algorithm to compute a subset of the dominating singular triplets. We also illustrate the connection of the method with inverse eigenvalue problems. In the Lanczos process, we use the so-called one-sided reorthogonalization strategy to maintain the orthogonality level of the Lanczos vec- tors. The efficiency and the applicability of our algorithm are illustrated by some numerical examples from information retrieval applications.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC03-76SF00098
- OSTI ID:
- 6451
- Report Number(s):
- LBNL-42472; ON: DE00006451
- Country of Publication:
- United States
- Language:
- English
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