NAG Library Routine Document
F11DEF
1 Purpose
F11DEF solves a real sparse nonsymmetric system of linear equations, represented in coordinate storage format, using a restarted generalized minimal residual (RGMRES), conjugate gradient squared (CGS), stabilized bi-conjugate gradient (Bi-CGSTAB), or transpose-free quasi-minimal residual (TFQMR) method, without preconditioning, with Jacobi, or with SSOR preconditioning.
2 Specification
SUBROUTINE F11DEF ( |
METHOD, PRECON, N, NNZ, A, IROW, ICOL, OMEGA, B, M, TOL, MAXITN, X, RNORM, ITN, WORK, LWORK, IWORK, IFAIL) |
INTEGER |
N, NNZ, IROW(NNZ), ICOL(NNZ), M, MAXITN, ITN, LWORK, IWORK(2*N+1), IFAIL |
REAL (KIND=nag_wp) |
A(NNZ), OMEGA, B(N), TOL, X(N), RNORM, WORK(LWORK) |
CHARACTER(*) |
METHOD |
CHARACTER(1) |
PRECON |
|
3 Description
F11DEF solves a real sparse nonsymmetric system of linear equations
using an RGMRES (see
Saad and Schultz (1986)), CGS (see
Sonneveld (1989)), Bi-CGSTAB(
) (see
Van der Vorst (1989) and
Sleijpen and Fokkema (1993)), or TFQMR (see
Freund and Nachtigal (1991) and
Freund (1993)) method.
The routine allows the following choices for the preconditioner:
- no preconditioning;
- Jacobi preconditioning (see Young (1971));
- symmetric successive-over-relaxation (SSOR) preconditioning (see Young (1971)).
For incomplete
(ILU) preconditioning see
F11DCF.
The matrix
is represented in coordinate storage (CS) format (see
Section 2.1.1 in the F11 Chapter Introduction) in the arrays
A,
IROW and
ICOL. The array
A holds the nonzero entries in the matrix, while
IROW and
ICOL hold the corresponding row and column indices.
F11DEF is a Black Box routine which calls
F11BDF,
F11BEF and
F11BFF. If you wish to use an alternative storage scheme, preconditioner, or termination criterion, or require additional diagnostic information, you should call these underlying routines directly.
4 References
Freund R W (1993) A transpose-free quasi-minimal residual algorithm for non-Hermitian linear systems SIAM J. Sci. Comput. 14 470–482
Freund R W and Nachtigal N (1991) QMR: a Quasi-Minimal Residual Method for Non-Hermitian Linear Systems Numer. Math. 60 315–339
Saad Y and Schultz M (1986) GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems SIAM J. Sci. Statist. Comput. 7 856–869
Sleijpen G L G and Fokkema D R (1993) BiCGSTAB for linear equations involving matrices with complex spectrum ETNA 1 11–32
Sonneveld P (1989) CGS, a fast Lanczos-type solver for nonsymmetric linear systems SIAM J. Sci. Statist. Comput. 10 36–52
Van der Vorst H (1989) Bi-CGSTAB, a fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems SIAM J. Sci. Statist. Comput. 13 631–644
Young D (1971) Iterative Solution of Large Linear Systems Academic Press, New York
5 Parameters
- 1: METHOD – CHARACTER(*)Input
On entry: the iterative method to be used.
- Restarted generalized minimum residual method.
- Conjugate gradient squared method.
- Bi-conjugate gradient stabilized () method.
- Transpose-free quasi-minimal residual method.
Constraint:
, , or .
- 2: PRECON – CHARACTER(1)Input
On entry: specifies the type of preconditioning to be used.
- No preconditioning.
- Jacobi.
- Symmetric successive-over-relaxation.
Constraint:
, or .
- 3: N – INTEGERInput
On entry: , the order of the matrix .
Constraint:
.
- 4: NNZ – INTEGERInput
On entry: the number of nonzero elements in the matrix .
Constraint:
.
- 5: A(NNZ) – REAL (KIND=nag_wp) arrayInput
On entry: the nonzero elements of the matrix
, 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
F11ZAF may be used to order the elements in this way.
- 6: IROW(NNZ) – INTEGER arrayInput
- 7: ICOL(NNZ) – INTEGER arrayInput
On entry: the row and column indices of the nonzero elements supplied in
A.
Constraints:
IROW and
ICOL must satisfy the following constraints (which may be imposed by a call to
F11ZAF):
- and , for ;
- or and , for .
- 8: OMEGA – REAL (KIND=nag_wp)Input
On entry: if
,
OMEGA is the relaxation parameter
to be used in the SSOR method. Otherwise
OMEGA need not be initialized and is not referenced.
Constraint:
.
- 9: B(N) – REAL (KIND=nag_wp) arrayInput
On entry: the right-hand side vector .
- 10: M – INTEGERInput
On entry: if
,
M is the dimension of the restart subspace.
If
,
M is the order
of the polynomial Bi-CGSTAB method.
Otherwise,
M is not referenced.
Constraints:
- if , ;
- if , .
- 11: TOL – REAL (KIND=nag_wp)Input
On entry: the required tolerance. Let
denote the approximate solution at iteration
, and
the corresponding residual. The algorithm is considered to have converged at iteration
if
If
,
is used, where
is the
machine precision. Otherwise
is used.
Constraint:
.
- 12: MAXITN – INTEGERInput
On entry: the maximum number of iterations allowed.
Constraint:
.
- 13: X(N) – REAL (KIND=nag_wp) arrayInput/Output
On entry: an initial approximation to the solution vector .
On exit: an improved approximation to the solution vector .
- 14: RNORM – REAL (KIND=nag_wp)Output
On exit: the final value of the residual norm
, where
is the output value of
ITN.
- 15: ITN – INTEGEROutput
On exit: the number of iterations carried out.
- 16: WORK(LWORK) – REAL (KIND=nag_wp) arrayWorkspace
- 17: LWORK – INTEGERInput
On entry: the dimension of the array
WORK as declared in the (sub)program from which F11DEF is called.
Constraints:
- if , ;
- if , ;
- if , ;
- if , .
where for or , and otherwise
- 18: IWORK() – INTEGER arrayWorkspace
- 19: IFAIL – INTEGERInput/Output
-
On entry:
IFAIL must be set to
,
. If you are unfamiliar with this parameter you should refer to
Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value
is recommended. If the output of error messages is undesirable, then the value
is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is
.
When the value is used it is essential to test the value of IFAIL on exit.
On exit:
unless the routine detects an error or a warning has been flagged (see
Section 6).
6 Error Indicators and Warnings
If on entry
or
, explanatory error messages are output on the current error message unit (as defined by
X04AAF).
Errors or warnings detected by the routine:
On entry, | , or 'TFQMR', |
or | , or , |
or | , |
or | , |
or | , |
or | and OMEGA lies outside the interval , |
or | , |
or | , with , |
or | , with , |
or | , |
or | , |
or | LWORK too small. |
On entry, the arrays
IROW and
ICOL fail to satisfy the following constraints:
- and , for ;
- , or and , for .
Therefore a nonzero element has been supplied which does not lie within the matrix
, is out of order, or has duplicate row and column indices. Call
F11ZAF to reorder and sum or remove duplicates.
On entry, the matrix has a zero diagonal element. Jacobi and SSOR preconditioners are not appropriate for this problem.
The required accuracy could not be obtained. However, a reasonable accuracy may have been obtained, and further iterations could not improve the result. You should check the output value of
RNORM for acceptability. This error code usually implies that your problem has been fully and satisfactorily solved to within or close to the accuracy available on your system. Further iterations are unlikely to improve on this situation.
Required accuracy not obtained in
MAXITN iterations.
Algorithmic breakdown. A solution is returned, although it is possible that it is completely inaccurate.
- (F11BDF, F11BEF or F11BFF)
A serious error has occurred in an internal call to one of the specified routines. Check all subroutine calls and array sizes. Seek expert help.
7 Accuracy
On successful termination, the final residual
, where
, satisfies the termination criterion
The value of the final residual norm is returned in
RNORM.
The time taken by F11DEF for each iteration is roughly proportional to
NNZ.
The number of iterations required to achieve a prescribed accuracy cannot be easily determined a priori, as it can depend dramatically on the conditioning and spectrum of the preconditioned coefficient matrix .
9 Example
This example solves a sparse nonsymmetric system of equations using the RGMRES method, with SSOR preconditioning.
9.1 Program Text
Program Text (f11defe.f90)
9.2 Program Data
Program Data (f11defe.d)
9.3 Program Results
Program Results (f11defe.r)