Singular Value Decomposition: ScaLAPACK Driver
Routines
This section describes ScaLAPACK routines for
computing the singular value decomposition (SVD) of a general
m
-by-n
matrix
A
(see
LAPACK
"Singular
Value Decomposition"
).
To find the SVD of a general matrix
A
, this matrix
is first reduced to a bidiagonal matrix
B
by a unitary
(orthogonal) transformation, and then SVD of the bidiagonal matrix is computed.
Note that the SVD of
B
is computed
using the LAPACK routine
?bdsqr
.
Table
"Computational Routines for Singular Value
Decomposition (SVD)"
lists ScaLAPACK computational routines for
performing this decomposition.