# NAG FL Interfacef08kdf (dgesdd)

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## 1Purpose

f08kdf computes the singular value decomposition (SVD) of a real $m×n$ matrix $A$, optionally computing the left and/or right singular vectors, by using a divide-and-conquer method.

## 2Specification

Fortran Interface
 Subroutine f08kdf ( jobz, m, n, a, lda, s, u, ldu, vt, ldvt, work, info)
 Integer, Intent (In) :: m, n, lda, ldu, ldvt, lwork Integer, Intent (Out) :: iwork(8*min(m,n)), info Real (Kind=nag_wp), Intent (Inout) :: a(lda,*), u(ldu,*), vt(ldvt,*) Real (Kind=nag_wp), Intent (Out) :: s(min(m,n)), work(max(1,lwork)) Character (1), Intent (In) :: jobz
#include <nag.h>
 void f08kdf_ (const char *jobz, const Integer *m, const Integer *n, double a[], const Integer *lda, double s[], double u[], const Integer *ldu, double vt[], const Integer *ldvt, double work[], const Integer *lwork, Integer iwork[], Integer *info, const Charlen length_jobz)
The routine may be called by the names f08kdf, nagf_lapackeig_dgesdd or its LAPACK name dgesdd.

## 3Description

The SVD is written as
 $A = UΣVT ,$
where $\Sigma$ is an $m×n$ matrix which is zero except for its $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ diagonal elements, $U$ is an $m×m$ orthogonal matrix, and $V$ is an $n×n$ 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 $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ columns of $U$ and $V$ are the left and right singular vectors of $A$.
Note that the routine returns ${V}^{\mathrm{T}}$, not $V$.

## 4References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia https://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

## 5Arguments

1: $\mathbf{jobz}$Character(1) Input
On entry: specifies options for computing all or part of the matrix $U$.
${\mathbf{jobz}}=\text{'A'}$
All $m$ columns of $U$ and all $n$ rows of ${V}^{\mathrm{T}}$ are returned in the arrays u and vt.
${\mathbf{jobz}}=\text{'S'}$
The first $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ columns of $U$ and the first $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ rows of ${V}^{\mathrm{T}}$ are returned in the arrays u and vt.
${\mathbf{jobz}}=\text{'O'}$
If ${\mathbf{m}}\ge {\mathbf{n}}$, the first $n$ columns of $U$ are overwritten on the array a and all rows of ${V}^{\mathrm{T}}$ are returned in the array vt. Otherwise, all columns of $U$ are returned in the array u and the first $m$ rows of ${V}^{\mathrm{T}}$ are overwritten in the array vt.
${\mathbf{jobz}}=\text{'N'}$
No columns of $U$ or rows of ${V}^{\mathrm{T}}$ are computed.
Constraint: ${\mathbf{jobz}}=\text{'A'}$, $\text{'S'}$, $\text{'O'}$ or $\text{'N'}$.
2: $\mathbf{m}$Integer Input
On entry: $m$, the number of rows of the matrix $A$.
Constraint: ${\mathbf{m}}\ge 0$.
3: $\mathbf{n}$Integer Input
On entry: $n$, the number of columns of the matrix $A$.
Constraint: ${\mathbf{n}}\ge 0$.
4: $\mathbf{a}\left({\mathbf{lda}},*\right)$Real (Kind=nag_wp) array Input/Output
Note: the second dimension of the array a must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: the $m×n$ matrix $A$.
On exit: if ${\mathbf{jobz}}=\text{'O'}$, a is overwritten with the first $n$ columns of $U$ (the left singular vectors, stored column-wise) if ${\mathbf{m}}\ge {\mathbf{n}}$; a is overwritten with the first $m$ rows of ${V}^{\mathrm{T}}$ (the right singular vectors, stored row-wise) otherwise.
If ${\mathbf{jobz}}\ne \text{'O'}$, the contents of a are destroyed.
5: $\mathbf{lda}$Integer Input
On entry: the first dimension of the array a as declared in the (sub)program from which f08kdf is called.
Constraint: ${\mathbf{lda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}\right)$.
6: $\mathbf{s}\left(\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$Real (Kind=nag_wp) array Output
On exit: the singular values of $A$, sorted so that ${\mathbf{s}}\left(i\right)\ge {\mathbf{s}}\left(i+1\right)$.
7: $\mathbf{u}\left({\mathbf{ldu}},*\right)$Real (Kind=nag_wp) array Output
Note: the second dimension of the array u must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}\right)$ if ${\mathbf{jobz}}=\text{'A'}$ or (${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}<{\mathbf{n}}$), $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$ if ${\mathbf{jobz}}=\text{'S'}$, and at least $1$ otherwise.
On exit:
If ${\mathbf{jobz}}=\text{'A'}$ or ${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}<{\mathbf{n}}$, u contains the $m×m$ orthogonal matrix $U$.
If ${\mathbf{jobz}}=\text{'S'}$, u contains the first $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ columns of $U$ (the left singular vectors, stored column-wise).
If ${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}\ge {\mathbf{n}}$, or ${\mathbf{jobz}}=\text{'N'}$, u is not referenced.
8: $\mathbf{ldu}$Integer Input
On entry: the first dimension of the array u as declared in the (sub)program from which f08kdf is called.
Constraints:
• if ${\mathbf{jobz}}=\text{'S'}$ or $\text{'A'}$ or (${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}<{\mathbf{n}}$), ${\mathbf{ldu}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}\right)$;
• otherwise ${\mathbf{ldu}}\ge 1$.
9: $\mathbf{vt}\left({\mathbf{ldvt}},*\right)$Real (Kind=nag_wp) array Output
Note: the second dimension of the array vt must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$ if ${\mathbf{jobz}}=\text{'A'}$ or $\text{'S'}$ or (${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}\ge {\mathbf{n}}$), and at least $1$ otherwise.
On exit: if ${\mathbf{jobz}}=\text{'A'}$ or ${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}\ge {\mathbf{n}}$, vt contains the $n×n$ orthogonal matrix ${V}^{\mathrm{T}}$.
If ${\mathbf{jobz}}=\text{'S'}$, vt contains the first $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ rows of ${V}^{\mathrm{T}}$ (the right singular vectors, stored row-wise).
If ${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}<{\mathbf{n}}$, or ${\mathbf{jobz}}=\text{'N'}$, vt is not referenced.
10: $\mathbf{ldvt}$Integer Input
On entry: the first dimension of the array vt as declared in the (sub)program from which f08kdf is called.
Constraints:
• if ${\mathbf{jobz}}=\text{'A'}$ or (${\mathbf{jobz}}=\text{'O'}$ and ${\mathbf{m}}\ge {\mathbf{n}}$), ${\mathbf{ldvt}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• if ${\mathbf{jobz}}=\text{'S'}$, ${\mathbf{ldvt}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$;
• otherwise ${\mathbf{ldvt}}\ge 1$.
11: $\mathbf{work}\left(\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{lwork}}\right)\right)$Real (Kind=nag_wp) array Workspace
12: $\mathbf{lwork}$Integer Input
On entry: the dimension of the array work as declared in the (sub)program from which f08kdf is called.
If ${\mathbf{lwork}}=-1$, a workspace query is assumed; the routine only calculates the optimal size of the work array, returns this value as the first entry of the work array, and no error message related to lwork is issued.
Suggested value: for optimal performance, lwork should generally be larger. Consider increasing lwork by at least $\mathit{nb}×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)$, where $\mathit{nb}$ is the optimal block size.
Constraints:
• if ${\mathbf{jobz}}=\text{'N'}$, ${\mathbf{lwork}}\ge 3×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)+\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right),7×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$;
• if ${\mathbf{jobz}}=\text{'O'}$, ${\mathbf{lwork}}\ge 3×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)+\phantom{\rule{0ex}{0ex}}\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right),5×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)+4×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$;
• if ${\mathbf{jobz}}=\text{'S'}$ or $\text{'A'}$, ${\mathbf{lwork}}\ge 3×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)+\phantom{\rule{0ex}{0ex}}\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right),4×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)+4×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$;
• otherwise ${\mathbf{lwork}}\ge 1$.
13: $\mathbf{iwork}\left(8×\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{m}},{\mathbf{n}}\right)\right)$Integer array Workspace
14: $\mathbf{info}$Integer Output
On exit: ${\mathbf{info}}=0$ unless the routine detects an error (see Section 6).

## 6Error Indicators and Warnings

${\mathbf{info}}<0$
If ${\mathbf{info}}=-i$, argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.
${\mathbf{info}}>0$
f08kdf did not converge, the updating process failed.

## 7Accuracy

The computed singular value decomposition is nearly the exact singular value decomposition for a nearby matrix $\left(A+E\right)$, where
 $‖E‖2 = O(ε) ‖A‖2 ,$
and $\epsilon$ is the machine precision. In addition, the computed singular vectors are nearly orthogonal to working precision. See Section 4.9 of Anderson et al. (1999) for further details.

## 8Parallelism and Performance

f08kdf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08kdf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

The total number of floating-point operations is approximately proportional to $m{n}^{2}$ when $m>n$ and ${m}^{2}n$ otherwise.
The singular values are returned in descending order.
The complex analogue of this routine is f08krf.

## 10Example

This example finds the singular values and left and right singular vectors of the $4×6$ matrix
 $A = ( 2.27 0.28 -0.48 1.07 -2.35 0.62 -1.54 -1.67 -3.09 1.22 2.93 -7.39 1.15 0.94 0.99 0.79 -1.45 1.03 -1.94 -0.78 -0.21 0.63 2.30 -2.57 ) ,$
together with approximate error bounds for the computed singular values and vectors.
The example program for f08kbf illustrates finding a singular value decomposition for the case $m\ge n$.

### 10.1Program Text

Program Text (f08kdfe.f90)

### 10.2Program Data

Program Data (f08kdfe.d)

### 10.3Program Results

Program Results (f08kdfe.r)