This section describes ScaLAPACK routines for computing the singular value decomposition (SVD) of a general
"Singular Value Decomposition").
To find the SVD of a general matrix
A, this matrix is first reduced to a bidiagonal matrix
Bby a unitary (orthogonal) transformation, and then SVD of the bidiagonal matrix is computed. Note that the SVD of
Bis computed using the LAPACK routine
"Computational Routines for Singular Value Decomposition (SVD)"lists ScaLAPACK computational routines for performing this decomposition.