NAG Library Routine Document

f11xnf computes a matrix-vector or conjugate transposed matrix-vector product involving a complex sparse non-Hermitian matrix stored in coordinate storage format.

2

Specification

Fortran Interface

Subroutine f11xnf (

trans, n, nnz, a, irow, icol, check, x, y, ifail)

Integer, Intent (In)	::	n, nnz, irow(nnz), icol(nnz)
Integer, Intent (Inout)	::	ifail
Complex (Kind=nag_wp), Intent (In)	::	a(nnz), x(n)
Complex (Kind=nag_wp), Intent (Out)	::	y(n)
Character (1), Intent (In)	::	trans, check

C Header Interface

#include <nagmk26.h>

void	f11xnf_ (const char trans, const Integer n, const Integer nnz, const Complex a[], const Integer irow[], const Integer icol[], const char check, const Complex x[], Complex y[], Integer *ifail, const Charlen length_trans, const Charlen length_check)

3

Description

f11xnf computes either the matrix-vector product

y = A x

, or the conjugate transposed matrix-vector product

y = A^{H} x

, according to the value of the argument trans, where

A

is a complex

n

n

sparse non-Hermitian matrix, of arbitrary sparsity pattern. The matrix

A

is stored in coordinate storage (CS) format (see Section 2.1.1 in the F11 Chapter Introduction). The array a stores all the nonzero elements of

A

, while arrays irow and icol store the corresponding row and column indices respectively.

It is envisaged that a common use of f11xnf will be to compute the matrix-vector product required in the application of f11bsf to sparse complex linear systems. This is illustrated in Section 10 in f11drf.

4

References

None.

5

Arguments

1: $trans$ – Character(1)Input

On entry: specifies whether or not the matrix

A

is conjugate transposed.

$trans ='N'$: $y = A x$ is computed.
$trans ='T'$: $y = A^{H} x$ is computed.

Constraint:

trans ='N'

'T'

2: $n$ – IntegerInput

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 1

3: $nnz$ – IntegerInput

On entry: the number of nonzero elements in the matrix

A

Constraint:

1 \leq nnz \leq n^{2}

4: $a (nnz)$ – Complex (Kind=nag_wp) arrayInput

On entry: the nonzero elements in the matrix

A

, ordered by increasing row index, and by increasing column index within each row. Multiple entries for the same row and column indices are not permitted. The routine f11znf may be used to order the elements in this way.

5: $irow (nnz)$ – Integer arrayInput

6: $icol (nnz)$ – Integer arrayInput

On entry: the row and column indices of the nonzero elements supplied in array a.

Constraints:

$1 \leq irow (i) \leq n$ and $1 \leq icol (i) \leq n$ , for $i = 1, 2, \dots, nnz$ ;
$irow (i - 1) < irow (i)$ or $irow (i - 1) = irow (i)$ and $icol (i - 1) < icol (i)$ , for $i = 2, 3, \dots, nnz$ .

7: $check$ – Character(1)Input

On entry: specifies whether or not the CS representation of the matrix

A

, values of n, nnz, irow and icol should be checked.

$check ='C'$: Checks are carried on the values of n, nnz, irow and icol.
$check ='N'$: None of these checks are carried out.

6

Error Indicators and Warnings

If on entry

ifail = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail = 1$: On entry, $check = 〈value〉$ .
Constraint: $check ='C'$ or $'N'$ .

On entry, $trans = 〈value〉$ .
Constraint: $trans ='N'$ or $'T'$ .

$ifail = 2$: On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .

On entry, $nnz = 〈value〉$ .
Constraint: $nnz \geq 1$ .

On entry, $nnz = 〈value〉$ and $n = 〈value〉$ .
Constraint: $nnz \leq n^{2}$ .

$ifail = 3$: On entry, $a (i)$ is out of order: $i = 〈value〉$ .

On entry, $i = 〈value〉$ , $icol (i) = 〈value〉$ and $n = 〈value〉$ .
Constraint: $icol (i) \geq 1$ and $icol (i) \leq n$ .

On entry, $i = 〈value〉$ , $irow (i) = 〈value〉$ and $n = 〈value〉$ .
Constraint: $irow (i) \geq 1$ and $irow (i) \leq n$ .

On entry, the location ( $irow (I), icol (I)$ ) is a duplicate: $I = 〈value〉$ .
A nonzero element has been supplied which does not lie within the matrix $A$ , is out of order, or has duplicate row and column indices. Consider calling f11znf to reorder and sum or remove duplicates.

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

The computed vector

y

satisfies the error bound:

${‖y - A x‖}_{\infty} \leq c (n) ε {‖A‖}_{\infty} {‖x‖}_{\infty}$ , if $trans ='N'$ , or
${‖y - A^{H} x‖}_{\infty} \leq c (n) ε {‖A^{H}‖}_{\infty} {‖x‖}_{\infty}$ , if $trans ='T'$ ,

where

c (n)

is a modest linear function of

n

, and

ε

is the machine precision.

8

Parallelism and Performance

f11xnf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f11xnf 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

9.1

Timing

The time taken for a call to f11xnf is proportional to nnz.

9.2

Use of check

It is expected that a common use of f11xnf will be to compute the matrix-vector product required in the application of f11bsf to sparse complex linear systems. In this situation f11xnf is likely to be called many times with the same matrix

A

. In the interests of both reliability and efficiency you are recommended to set

check ='C'

for the first of such calls, and to set

check ='N'

for all subsequent calls.

10

Example

This example reads in a complex sparse matrix

A

and a vector

x

. It then calls f11xnf to compute the matrix-vector product

y = A x

and the conjugate transposed matrix-vector product

y = A^{H} x

NAG Library Routine Document

f11xnf (complex_gen_matvec)

▸▿ 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

9.1 Timing

9.2 Use of check

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

1

Purpose

2

Specification

3

Description

4

References

5

Arguments

6

Error Indicators and Warnings

7

Accuracy

8

Parallelism and Performance

9

Further Comments

9.1

Timing

9.2

Use of check

10

Example

10.1

Program Text

10.2

Program Data

10.3

Program Results