Developer Reference for Intel® oneAPI Math Kernel Library - C

Developer Reference

  • 0.9
  • 09/09/2020
  • Public Content
Contents

Generalized Singular Value Decomposition: LAPACK Computational Routines

This
topic
describes LAPACK computational routines used for finding the generalized singular value decomposition (GSVD) of two matrices
A
 and
B
 as
U
H
AQ
 =
D
1
*(0
R
)
,
V
H
BQ
 =
D
2
*(0
R
)
,
where
U
,
V
, and
Q
 are orthogonal/unitary matrices,
R
 is a nonsingular upper triangular matrix, and
D
1
,
D
2
 are “diagonal” matrices of the structure detailed in the routines description section.
Table
“Computational Routines for Generalized Singular Value Decomposition”
 lists LAPACK routines that perform generalized singular value decomposition of matrices.
Computational Routines for Generalized Singular Value Decomposition
Routine name
Operation performed
Computes the preprocessing decomposition for the generalized SVD
Performs preprocessing for a generalized SVD.
Computes generalized SVD.
Computes the generalized SVD of two upper triangular or trapezoidal matrices
You can use routines listed in the above table as well as the driver routine ggsvd to find the GSVD of a pair of general rectangular matrices.

Product and Performance Information

1

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