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.