Abstract
SVDPACK comprises four numerical (iterative) methods for computing the singular value decomposition (SVD) of large sparse matrices using double precision ANSI Fortran-77. This software package implements Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left- and right-singular vectors) for large sparse matrices. The package has been ported to a variety of machines ranging from supercomputers to workstations: CRAY Y-MP, CRAY-2S, Alliant FX/80, IBM RS/6000-550, DEC 5000-100, and Sun SPARCstation 2. The performance of SVDPACK as measured by its use in computing large rank approximations to sparse term-document matrices from information retrieval applications, and on synthetically-generated matrices having clustered and multiple singular values is presented.
This research was supported by the National Science Foundation under grant NSF CDA-9115428, Apple Computer Inc., Cupertino, CA, under contract C24-9100120, and by the Institute for Mathematics and its Applications with funds provided by the National Science Foundation.
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© 1994 Springer-Verlag New York, Inc
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Berry, M.W. (1994). Computing the Sparse Singular Value Decomposition via SVDPACK. In: Golub, G., Luskin, M., Greenbaum, A. (eds) Recent Advances in Iterative Methods. The IMA Volumes in Mathematics and its Applications, vol 60. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9353-5_2
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DOI: https://doi.org/10.1007/978-1-4613-9353-5_2
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