Integer, Intent (In)	::	m, n, lda, ldu, ldvt, lwork
Integer, Intent (Out)	::	info
Real (Kind=nag_wp), Intent (Inout)	::	s(), rwork()
Complex (Kind=nag_wp), Intent (Inout)	::	a(lda,), u(ldu,), vt(ldvt,*)
Complex (Kind=nag_wp), Intent (Out)	::	work(max(1,lwork))
Character (1), Intent (In)	::	jobu, jobvt

C Header Interface

#include <nag.h>

void

f08kpf_ (const char *jobu, const char *jobvt, const Integer *m, const Integer *n, Complex a[], const Integer *lda, double s[], Complex u[], const Integer *ldu, Complex vt[], const Integer *ldvt, Complex work[], const Integer *lwork, double rwork[], Integer *info, const Charlen length_jobu, const Charlen length_jobvt)

The routine may be called by the names f08kpf, nagf_lapackeig_zgesvd or its LAPACK name zgesvd.

3 Description

The SVD is written as

A = U Σ V^{H},

where

Σ

is an

m \times n

matrix which is zero except for its

\min (m, n)

diagonal elements,

U

is an

m \times m

unitary matrix, and

V

is an

n \times n

unitary matrix. The diagonal elements of

Σ

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

Note that the routine returns

V^{H}

, not

V

4 References

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

5 Arguments

1: $jobu$ – Character(1) Input

On entry: specifies options for computing all or part of the matrix

U

$jobu ='A'$: All $m$ columns of $U$ are returned in array u.
$jobu ='S'$: The first $\min (m, n)$ columns of $U$ (the left singular vectors) are returned in the array u.
$jobu ='O'$: The first $\min (m, n)$ columns of $U$ (the left singular vectors) are overwritten on the array a.
$jobu ='N'$: No columns of $U$ (no left singular vectors) are computed.

Constraint:

jobu ='A'

'S'

'O'

'N'

2: $jobvt$ – Character(1) Input

On entry: specifies options for computing all or part of the matrix

V^{H}

$jobvt ='A'$: All $n$ rows of $V^{H}$ are returned in the array vt.
$jobvt ='S'$: The first $\min (m, n)$ rows of $V^{H}$ (the right singular vectors) are returned in the array vt.
$jobvt ='O'$: The first $\min (m, n)$ rows of $V^{H}$ (the right singular vectors) are overwritten on the array a.
$jobvt ='N'$: No rows of $V^{H}$ (no right singular vectors) are computed.

Constraints:

$jobvt ='A'$ , $'S'$ , $'O'$ or $'N'$ ;
$jobvt \neq'O'$ or $jobu \neq'O'$ .

3: $m$ – Integer Input

On entry:

m

, the number of rows of the matrix

A

Constraint:

m \geq 0

4: $n$ – Integer Input

On entry:

n

, the number of columns of the matrix

A

Constraint:

n \geq 0

5: $a (lda, *)$ – Complex (Kind=nag_wp) array Input/Output

Note: the second dimension of the array a must be at least

\max (1, n)

On entry: the

m \times n

matrix

A

On exit: if

jobu ='O'

, a is overwritten with the first

\min (m, n)

columns of

U

(the left singular vectors, stored column-wise).

jobvt ='O'

, a is overwritten with the first

\min (m, n)

rows of

V^{H}

(the right singular vectors, stored row-wise).

jobu \neq'O'

and

jobvt \neq'O'

, the contents of a are destroyed.

6: $lda$ – Integer Input

On entry: the first dimension of the array a as declared in the (sub)program from which f08kpf is called.

Constraint:

lda \geq \max (1, m)

7: $s (*)$ – Real (Kind=nag_wp) array Output

Note: the dimension of the array s must be at least

\max (1, \min (m, n))

On exit: the singular values of

A

, sorted so that

s (i) \geq s (i + 1)

8: $u (ldu, *)$ – Complex (Kind=nag_wp) array Output

Note: the second dimension of the array u must be at least

\max (1, m)

jobu ='A'

\max (1, \min (m, n))

jobu ='S'

, and at least

1

otherwise.

On exit: if

jobu ='A'

, u contains the

m \times m

unitary matrix

U

jobu ='S'

, u contains the first

\min (m, n)

columns of

U

(the left singular vectors, stored column-wise).

jobu ='N'

'O'

, u is not referenced.

9: $ldu$ – Integer Input

On entry: the first dimension of the array u as declared in the (sub)program from which f08kpf is called.

Constraints:

if $jobu ='A'$ or $'S'$ , $ldu \geq \max (1, m)$ ;
otherwise $ldu \geq 1$ .

10: $vt (ldvt, *)$ – Complex (Kind=nag_wp) array Output

Note: the second dimension of the array vt must be at least

\max (1, n)

jobvt ='A'

'S'

, and at least

1

otherwise.

On exit: if

jobvt ='A'

, vt contains the

n \times n

unitary matrix

V^{H}

jobvt ='S'

, vt contains the first

\min (m, n)

rows of

V^{H}

(the right singular vectors, stored row-wise).

jobvt ='N'

'O'

, vt is not referenced.

11: $ldvt$ – Integer Input

On entry: the first dimension of the array vt as declared in the (sub)program from which f08kpf is called.

Constraints:

if $jobvt ='A'$ , $ldvt \geq \max (1, n)$ ;
if $jobvt ='S'$ , $ldvt \geq \max (1, \min (m, n))$ ;
otherwise $ldvt \geq 1$ .

12: $work (\max (1, lwork))$ – Complex (Kind=nag_wp) array Workspace

On exit: if

info = 0

, the real part of

work (1)

contains the minimum value of lwork required for optimal performance.

13: $lwork$ – Integer Input

On entry: the dimension of the array work as declared in the (sub)program from which f08kpf is called.

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

nb \times \min (m, n)

, where

nb

is the optimal block size.

Constraint:

lwork \geq \max (1, 2 \times \min (m, n) + \max (m, n))

14: $rwork (*)$ – Real (Kind=nag_wp) array Workspace

Note: the dimension of the array rwork must be at least

\max (1, 5 \times \min (m, n))

On exit: if

info > 0

rwork (1 : \min (m, n) - 1)

contains the unconverged superdiagonal elements of an upper bidiagonal matrix

B

whose diagonal is in

S

(not necessarily sorted).

B

satisfies

A = U B V^{H}

, so it has the same singular values as

A

, and singular vectors related by

U

and

V^{H}

15: $info$ – Integer Output

On exit:

info = 0

unless the routine detects an error (see Section 6).

6 Error Indicators and Warnings

$info < 0$: If $info = - i$ , argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.

$info > 0$: If f08kpf did not converge, info specifies how many superdiagonals of an intermediate bidiagonal form did not converge to zero.

7 Accuracy

The computed singular value decomposition is nearly the exact singular value decomposition for a nearby matrix

(A + E)

, where

{‖ E ‖}_{2} = O (ε) {‖ A ‖}_{2},

and

ε

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.

8 Parallelism and Performance

f08kpf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f08kpf 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.

9 Further Comments

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 real analogue of this routine is f08kbf.

10 Example

This example finds the singular values and left and right singular vectors of the

6 \times 4

matrix

A = (\begin{array}{r} 0.96 - 0.81 i & - 0.03 + 0.96 i & - 0.91 + 2.06 i & - 0.05 + 0.41 i \\ - 0.98 + 1.98 i & - 1.20 + 0.19 i & - 0.66 + 0.42 i & - 0.81 + 0.56 i \\ 0.62 - 0.46 i & 1.01 + 0.02 i & 0.63 - 0.17 i & - 1.11 + 0.60 i \\ - 0.37 + 0.38 i & 0.19 - 0.54 i & - 0.98 - 0.36 i & 0.22 - 0.20 i \\ 0.83 + 0.51 i & 0.20 + 0.01 i & - 0.17 - 0.46 i & 1.47 + 1.59 i \\ 1.08 - 0.28 i & 0.20 - 0.12 i & - 0.07 + 1.23 i & 0.26 + 0.26 i \end{array}),

together with approximate error bounds for the computed singular values and vectors.

The example program for f08krf illustrates finding a singular value decomposition for the case

m \leq n

f08kp: FL CL CPP AD

NAG FL Interfacef08kpf (zgesvd)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
f08kpf (zgesvd)