An implicitly restarted bidiagonal Lanczos Method forLarge-scale singular value problems
- LBNL Library
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:
- Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (US)
- Sponsoring Organization:
- USDOE Office of Science
- DOE Contract Number:
- AC03-76SF00098;
- OSTI ID:
- 6451
- Report Number(s):
- LBNL-42472; ON: DE00006451
- Country of Publication:
- United States
- Language:
- English
Similar Records
Computing small singular values of bidiagonal matrices with guaranteed high relative accuracy: LAPACK working note number 3
Algorithms for sparse matrix eigenvalue problems. [DBLKLN, block Lanczos algorithm with local reorthogonalization strategy]
Lanczos algorithm for symmetric eigenvalue problems
Technical Report
·
Mon Feb 01 00:00:00 UTC 1988
·
OSTI ID:5039344
Algorithms for sparse matrix eigenvalue problems. [DBLKLN, block Lanczos algorithm with local reorthogonalization strategy]
Technical Report
·
Tue Mar 01 00:00:00 UTC 1977
·
OSTI ID:7254102
Lanczos algorithm for symmetric eigenvalue problems
Conference
·
Tue Jan 01 00:00:00 UTC 1980
·
OSTI ID:5171549