SVDPACK comprises four numerical (iterative) methods
for computing the singular value decomposition (SVD)
of large sparse matrices using double precision ANSI Fortran-77.
A compatible ANSI-C version (SVDPACKC) is also available.
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, SPARCstation 10,
IBM RS/6000-550, DEC 5000-100, and HP 9000-750. The development
of SVDPACK wa motivated by the need to compute large rank approximations
to sparse term-document matrices from information retrieval applications.
Future updates to SVDPACK(C), will include
out-of-core updating strategies, which can be used,
for example, to handle extremely large sparse matrices (on the
order of a million rows or columns)
associated with extremely large databases
in query-based information retrieval applications.
The email address svdpack@cs.utk.edu may be used for
general discussions. Comments and questions may also be sent
to the author at berry@cs.utk.edu.