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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.
Computational Routines for Singular Value Decomposition (SVD)
Operation
General matrix
Orthogonal/unitary matrix
Reduce
A
to a bidiagonal matrix
 
Multiply matrix after reduction
 

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