nag_sparse_nsym_precon_ssor_solve (f11ddc) solves a system of linear equations involving the preconditioning matrix corresponding to SSOR applied to a real sparse nonsymmetric matrix, represented in coordinate storage format.

2

Specification

#include <nag.h>

#include <nagf11.h>

void	nag_sparse_nsym_precon_ssor_solve (Nag_TransType trans, Integer n, Integer nnz, const double a[], const Integer irow[], const Integer icol[], const double rdiag[], double omega, Nag_SparseNsym_CheckData check, const double y[], double x[], NagError *fail)

3

Description

nag_sparse_nsym_precon_ssor_solve (f11ddc) solves a system of linear equations

\begin{matrix} M x = y, & or & M^{T} x = y, \end{matrix}

according to the value of the argument trans, where the matrix

M = \frac{1}{ω (2 - ω)} (D + ω L) D^{- 1} (D + ω U)

corresponds to symmetric successive-over-relaxation (SSOR) (see Young (1971)) applied to a linear system

A x = b

, where

A

is a real sparse nonsymmetric matrix stored in coordinate storage (CS) format (see Section 2.1.1 in the f11 Chapter Introduction).

In the definition of

M

given above

D

is the diagonal part of

A

L

is the strictly lower triangular part of

A

U

is the strictly upper triangular part of

A

, and

ω

is a user-defined relaxation parameter.

It is envisaged that a common use of nag_sparse_nsym_precon_ssor_solve (f11ddc) will be to carry out the preconditioning step required in the application of nag_sparse_nsym_basic_solver (f11bec) to sparse linear systems. For an illustration of this use of nag_sparse_nsym_precon_ssor_solve (f11ddc) see the example program given in Section 10. nag_sparse_nsym_precon_ssor_solve (f11ddc) is also used for this purpose by the Black Box function nag_sparse_nsym_sol (f11dec).

4

References

Young D (1971) Iterative Solution of Large Linear Systems Academic Press, New York

5

Arguments

1: $trans$ – Nag_TransTypeInput

On entry: specifies whether or not the matrix

M

is transposed.

$trans = Nag_NoTrans$: $M x = y$ is solved.
$trans = Nag_Trans$: $M^{T} x = y$ is solved.

Constraint:

trans = Nag_NoTrans

Nag_Trans

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 doubleInput

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_nsym_sort (f11zac) 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:

irow and icol must satisfy the following constraints (which may be imposed by a call to nag_sparse_nsym_sort (f11zac)):

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

7: $rdiag [n]$ – const doubleInput

On entry: the elements of the diagonal matrix

D^{- 1}

, where

D

is the diagonal part of

A

8: $omega$ – doubleInput

On entry: the relaxation parameter

ω

Constraint:

0.0 < omega < 2.0

9: $check$ – Nag_SparseNsym_CheckDataInput

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

M

should be checked.

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

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation 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: $1 \leq nnz \leq n^{2}$ .
NE_INT_2: On entry, $nnz = 〈value〉$ and $n = 〈value〉$ .
Constraint: $1 \leq nnz \leq n^{2}$ .
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.
See Section 2.7.6 in How to Use the NAG Library and its Documentation 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 2.7.5 in How to Use the NAG Library and its Documentation 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: $I = 〈value〉$ .
NE_REAL: On entry, $omega = 〈value〉$ .
Constraint: $0.0 < omega < 2.0$ .
NE_ZERO_DIAG_ELEM: The matrix $A$ has no diagonal entry in row $〈value〉$ .

7

Accuracy

trans = Nag_NoTrans

the computed solution

x

is the exact solution of a perturbed system of equations

(M + δ M) x = y

, where

|δ M| \leq c (n) ε |D + ω L| |D^{- 1}| |D + ω U|,

c (n)

is a modest linear function of

n

, and

ε

is the machine precision. An equivalent result holds when

trans = Nag_Trans

8

Parallelism and Performance

nag_sparse_nsym_precon_ssor_solve (f11ddc) is not threaded in any implementation.

9

Further Comments

9.1

Timing

The time taken for a call to nag_sparse_nsym_precon_ssor_solve (f11ddc) is proportional to nnz.

9.2

Use of check

It is expected that a common use of nag_sparse_nsym_precon_ssor_solve (f11ddc) will be to carry out the preconditioning step required in the application of nag_sparse_nsym_basic_solver (f11bec) to sparse linear systems. In this situation nag_sparse_nsym_precon_ssor_solve (f11ddc) is likely to be called many times with the same matrix

M

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

check = Nag_SparseNsym_Check

for the first of such calls, and for all subsequent calls set

check = Nag_SparseNsym_NoCheck

10

Example

This example solves a sparse linear system of equations:

A x = b,

using RGMRES with SSOR preconditioning.

The RGMRES algorithm itself is implemented by the reverse communication function nag_sparse_nsym_basic_solver (f11bec), which returns repeatedly to the calling program with various values of the argument irevcm. This argument indicates the action to be taken by the calling program.

If $irevcm = 1$ , a matrix-vector product $v = A u$ is required. This is implemented by a call to nag_sparse_nsym_matvec (f11xac).
If $irevcm = - 1$ , a transposed matrix-vector product $v = A^{T} u$ is required in the estimation of the norm of $A$ . This is implemented by a call to nag_sparse_nsym_matvec (f11xac).
If $irevcm = 2$ , a solution of the preconditioning equation $M v = u$ is required. This is achieved by a call to nag_sparse_nsym_precon_ssor_solve (f11ddc).
If $irevcm = 4$ , nag_sparse_nsym_basic_solver (f11bec) has completed its tasks. Either the iteration has terminated, or an error condition has arisen.

For further details see the function document for nag_sparse_nsym_basic_solver (f11bec).

NAG Library Function Document

nag_sparse_nsym_precon_ssor_solve (f11ddc)

▸▿ 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