Intel® oneAPI Math Kernel Library LAPACK Examples

Developer Reference

  • 2022.1
  • 12/20/2021
  • Public Content
Contents

DGESDD Example

The routine computes the singular value decomposition (SVD) of a rectangular real matrix
A
, optionally the left and/or right singular vectors. This routine uses a divide and conquer algorithm to compute SVD.
The SVD is written as: 
A = U*SIGMA*V
T
where
  • A
     is a real
    m
    -by-
    n
     matrix.
  • SIGMA
     is an
    m
    -by-
    n
     matrix which is zero except for its
    min(
    m
    ,
    n
    )
     diagonal elements.
  • U
     is an
    m
    -by-
    m
     orthogonal matrix.
  • V
    T
     (
    V
     transposed) is an
    n
    -by-
    n
     orthogonal matrix.
The diagonal elements of
SIGMA
 are the singular values of
A
; they are real and non-negative, and are returned in descending order. The first
min(
m
,
n
)
 columns of
U
 and
V
 are the left and right singular vectors of
A
.
The routine returns
V
T
, not
V
.

C Interface

Please refer to the Matrix Layout section of the C Interface Conventions for the description of Row Major (C default) and Column Major (Fortran default).

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.