NAG Library Function Document

nag_sparse_nherm_matvec (f11xnc)

▸▿ 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

nag_sparse_nherm_matvec (f11xnc) computes a matrix-vector or conjugate transposed matrix-vector product involving a complex sparse non-Hermitian matrix stored in coordinate storage format.

2 Specification

#include <nag.h>

#include <nagf11.h>

void	nag_sparse_nherm_matvec (Nag_TransType trans, Integer n, Integer nnz, const Complex a[], const Integer irow[], const Integer icol[], Nag_SparseNsym_CheckData check, const Complex x[], Complex y[], NagError *fail)

3 Description

nag_sparse_nherm_matvec (f11xnc) 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 nag_sparse_nherm_matvec (f11xnc) will be to compute the matrix-vector product required in the application of nag_sparse_nherm_basic_solver (f11bsc) to sparse complex linear systems. This is illustrated in Section 10 in nag_sparse_nherm_precon_ssor_solve (f11drc).

4 References

None.

5 Arguments

1: $trans$ – Nag_TransTypeInput

On entry: specifies whether or not the matrix

A

is conjugate transposed.

$trans = Nag_NoTrans$: $y = A x$ is computed.
$trans = Nag_ConjTrans$: $y = A^{H} x$ is computed.

Constraint:

trans = Nag_NoTrans

Nag_ConjTrans

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: $a [nnz]$ – const ComplexInput

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 function nag_sparse_nherm_sort (f11znc) may be used to order the elements in this way.

5: $irow [nnz]$ – const IntegerInput

6: $icol [nnz]$ – const IntegerInput

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 $i = 0, 1, \dots, nnz - 1$ ;
$irow [i - 1] < irow [i]$ or $irow [i - 1] = irow [i]$ and $icol [i - 1] < icol [i]$ , for $i = 1, 2, \dots, nnz - 1$ .

7: $check$ – Nag_SparseNsym_CheckDataInput

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

A

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

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

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.2.1.2 in the Essential Introduction for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_INT: On entry, $n = 〈value〉$ .
Constraint: $n \geq 1$ .

On entry, $nnz = 〈value〉$ .
Constraint: $nnz \geq 1$ .
NE_INT_2: On entry, $nnz = 〈value〉$ and $n = 〈value〉$ .
Constraint: $nnz \leq n^{2}$ .
NE_INTERNAL_ERROR: An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.

An unexpected error has been triggered by this function. Please contact NAG.
See Section 3.6.6 in the Essential Introduction for further information.
NE_INVALID_CS: On entry, $i = 〈value〉$ , $icol [i - 1] = 〈value〉$ and $n = 〈value〉$ .
Constraint: $icol [i - 1] \geq 1$ and $icol [i - 1] \leq n$ .

On entry, $i = 〈value〉$ , $irow [i - 1] = 〈value〉$ and $n = 〈value〉$ .
Constraint: $irow [i - 1] \geq 1$ and $irow [i - 1] \leq n$ .
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 3.6.5 in the Essential Introduction for further information.
NE_NOT_STRICTLY_INCREASING: On entry, $a [i - 1]$ is out of order: $i = 〈value〉$ .

On entry, the location ( $irow [I - 1], icol [I - 1]$ ) is a duplicate: $I = 〈value〉$ . Consider calling nag_sparse_nherm_sort (f11znc) to reorder and sum or remove duplicates.

7 Accuracy

The computed vector

y

satisfies the error bound:

${‖y - A x‖}_{\infty} \leq c (n) ε {‖A‖}_{\infty} {‖x‖}_{\infty}$ , if $trans = Nag_NoTrans$ , or
${‖y - A^{H} x‖}_{\infty} \leq c (n) ε {‖A^{H}‖}_{\infty} {‖x‖}_{\infty}$ , if $trans = Nag_ConjTrans$ ,

where

c (n)

is a modest linear function of

n

, and

ε

is the machine precision.

8 Parallelism and Performance

nag_sparse_nherm_matvec (f11xnc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_sparse_nherm_matvec (f11xnc) 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 function. 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 nag_sparse_nherm_matvec (f11xnc) is proportional to nnz.

9.2 Use of check

It is expected that a common use of nag_sparse_nherm_matvec (f11xnc) will be to compute the matrix-vector product required in the application of nag_sparse_nherm_basic_solver (f11bsc) to sparse complex linear systems. In this situation nag_sparse_nherm_matvec (f11xnc) 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 = Nag_SparseNsym_Check

for the first of such calls, and to set

check = Nag_SparseNsym_NoCheck

for all subsequent calls.

10 Example

This example reads in a complex sparse matrix

A

and a vector

x

. It then calls nag_sparse_nherm_matvec (f11xnc) to compute the matrix-vector product

y = A x

and the conjugate transposed matrix-vector product

y = A^{H} x

NAG Library Function Documentnag_sparse_nherm_matvec (f11xnc)

▸▿ 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

NAG Library Function Document

nag_sparse_nherm_matvec (f11xnc)