NASA Logo

NTRS

NTRS - NASA Technical Reports Server

Back to Results
Large-scale sparse singular value computationsFour numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture are presented. Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left and right-singular vectors) for sparse matrices arising from two practical applications: information retrieval and seismic reflection tomography are emphasized. The target architectures for implementations are the CRAY-2S/4-128 and Alliant FX/80. The sparse SVD problem is well motivated by recent information-retrieval techniques in which dominant singular values and their corresponding singular vectors of large sparse term-document matrices are desired, and by nonlinear inverse problems from seismic tomography applications which require approximate pseudo-inverses of large sparse Jacobian matrices.
Document ID
19920052912
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Berry, Michael W.
(Tennessee, University Knoxville, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1992
Publication Information
Publication: International Journal of Supercomputer Applications
Volume: 6
Issue: 1 Sp
ISSN: 0890-2720
Subject Category
Computer Programming And Software
Accession Number
92A35536
Funding Number(s)
CONTRACT_GRANT: NSF CCR-90-0000N
CONTRACT_GRANT: NSF CCR-87-17492
CONTRACT_GRANT: AF-AFOSR-90-0044
CONTRACT_GRANT: NCC2-559
CONTRACT_GRANT: DE-FG02-85ER-25001
Distribution Limits
Public
Copyright
Other

Available Downloads

There are no available downloads for this record.
No Preview Available