NAG FL Interface
f11dqf (complex_gen_solve_ilu)
1
Purpose
f11dqf solves a complex sparse non-Hermitian 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, with incomplete preconditioning.
2
Specification
Fortran Interface
Subroutine f11dqf ( |
method, n, nnz, a, la, irow, icol, ipivp, ipivq, istr, idiag, b, m, tol, maxitn, x, rnorm, itn, work, lwork, ifail) |
Integer, Intent (In) |
:: |
n, nnz, la, irow(la), icol(la), istr(n+1), idiag(n), m, maxitn, lwork |
Integer, Intent (Inout) |
:: |
ipivp(n), ipivq(n), ifail |
Integer, Intent (Out) |
:: |
itn |
Real (Kind=nag_wp), Intent (In) |
:: |
tol |
Real (Kind=nag_wp), Intent (Out) |
:: |
rnorm |
Complex (Kind=nag_wp), Intent (In) |
:: |
a(la), b(n) |
Complex (Kind=nag_wp), Intent (Inout) |
:: |
x(n) |
Complex (Kind=nag_wp), Intent (Out) |
:: |
work(lwork) |
Character (*), Intent (In) |
:: |
method |
|
C Header Interface
#include <nag.h>
void |
f11dqf_ (const char *method, const Integer *n, const Integer *nnz, const Complex a[], const Integer *la, const Integer irow[], const Integer icol[], Integer ipivp[], Integer ipivq[], const Integer istr[], const Integer idiag[], const Complex b[], const Integer *m, const double *tol, const Integer *maxitn, Complex x[], double *rnorm, Integer *itn, Complex work[], const Integer *lwork, Integer *ifail, const Charlen length_method) |
|
C++ Header Interface
#include <nag.h> extern "C" {
void |
f11dqf_ (const char *method, const Integer &n, const Integer &nnz, const Complex a[], const Integer &la, const Integer irow[], const Integer icol[], Integer ipivp[], Integer ipivq[], const Integer istr[], const Integer idiag[], const Complex b[], const Integer &m, const double &tol, const Integer &maxitn, Complex x[], double &rnorm, Integer &itn, Complex work[], const Integer &lwork, Integer &ifail, const Charlen length_method) |
}
|
The routine may be called by the names f11dqf or nagf_sparse_complex_gen_solve_ilu.
3
Description
f11dqf solves a complex sparse non-Hermitian linear system of equations
using a preconditioned 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.
f11dqf uses the incomplete
factorization determined by
f11dnf as the preconditioning matrix. A call to
f11dqf must always be preceded by a call to
f11dnf. Alternative preconditioners for the same storage scheme are available by calling
f11dsf.
The matrix
, and the preconditioning matrix
, are represented in coordinate storage (CS) format (see
Section 2.1.1 in the
F11 Chapter Introduction) in the arrays
a,
irow and
icol, as returned from
f11dnf. The array
a holds the nonzero entries in these matrices, while
irow and
icol hold the corresponding row and column indices.
f11dqf is a Black Box routine which calls
f11brf,
f11bsf and
f11btf. 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
5
Arguments
-
1:
– Character(*)
Input
-
On entry: specifies 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:
– Integer
Input
-
On entry:
, the order of the matrix
. This
must be the same value as was supplied in the preceding call to
f11dnf.
Constraint:
.
-
3:
– Integer
Input
-
On entry: the number of nonzero elements in the matrix
. This
must be the same value as was supplied in the preceding call to
f11dnf.
Constraint:
.
-
4:
– Complex (Kind=nag_wp) array
Input
-
On entry: the values returned in the array
a by a previous call to
f11dnf.
-
5:
– Integer
Input
-
On entry: the dimension of the arrays
a,
irow and
icol as declared in the (sub)program from which
f11dqf is called. This
must be the same value as was supplied in the preceding call to
f11dnf.
Constraint:
.
-
6:
– Integer array
Input
-
7:
– Integer array
Input
-
8:
– Integer array
Input
-
9:
– Integer array
Input
-
10:
– Integer array
Input
-
11:
– Integer array
Input
-
On entry: the values returned in arrays
irow,
icol,
ipivp,
ipivq,
istr and
idiag by a previous call to
f11dnf.
ipivp and
ipivq are restored on exit.
-
12:
– Complex (Kind=nag_wp) array
Input
-
On entry: the right-hand side vector .
-
13:
– Integer
Input
-
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 , .
-
14:
– 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:
.
-
15:
– Integer
Input
-
On entry: the maximum number of iterations allowed.
Constraint:
.
-
16:
– Complex (Kind=nag_wp) array
Input/Output
-
On entry: an initial approximation to the solution vector .
On exit: an improved approximation to the solution vector .
-
17:
– Real (Kind=nag_wp)
Output
-
On exit: the final value of the residual norm
, where
is the output value of
itn.
-
18:
– Integer
Output
-
On exit: the number of iterations carried out.
-
19:
– Complex (Kind=nag_wp) array
Workspace
-
20:
– Integer
Input
-
On entry: the dimension of the array
work as declared in the (sub)program from which
f11dqf is called.
Constraints:
- if , ;
- if , ;
- if , ;
- if , .
-
21:
– Integer
Input/Output
-
On entry:
ifail must be set to
,
or
to set behaviour on detection of an error; these values have no effect when no error is detected.
A value of causes the printing of an error message and program execution will be halted; otherwise program execution continues. A value of means that an error message is printed while a value of means that it is not.
If halting is not appropriate, the value
or
is recommended. If message printing is undesirable, then the value
is recommended. Otherwise, the value
is recommended.
When the value or 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, and .
Constraint: .
On entry,
lwork is too small:
. Minimum required value of
.
On entry, and .
Constraint: and .
On entry, .
Constraint: .
On entry, .
Constraint: , or .
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, and .
Constraint: .
On entry, .
Constraint: .
-
On entry, is out of order: .
On entry, , , and .
Constraint: and .
On entry, , , .
Constraint: and .
On entry, the location () is a duplicate: .
Check that
a,
irow,
icol,
ipivp,
ipivq,
istr and
idiag have not been corrupted between calls to
f11dqf and
f11dnf.
-
The CS representation of the preconditioner is invalid.
Check that
a,
irow,
icol,
ipivp,
ipivq,
istr and
idiag have not been corrupted between calls to
f11dnf and
f11dqf.
-
The required accuracy could not be obtained. However, a reasonable accuracy may have been achieved.
-
The solution has not converged after iterations.
-
Algorithmic breakdown. A solution is returned, although it is possible that it is completely inaccurate.
-
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.
A serious error, code , has occurred in an internal call. Check all subroutine calls and array sizes. Seek expert help.
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 7 in the Introduction to the NAG Library FL Interface for further information.
Your licence key may have expired or may not have been installed correctly.
See
Section 8 in the Introduction to the NAG Library FL Interface for further information.
Dynamic memory allocation failed.
See
Section 9 in the Introduction to the NAG Library FL Interface for further information.
7
Accuracy
On successful termination, the final residual
, where
, satisfies the termination criterion
The value of the final residual norm is returned in
rnorm.
8
Parallelism and Performance
f11dqf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f11dqf 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.
The time taken by
f11dqf for each iteration is roughly proportional to the value of
nnzc returned from the preceding call to
f11dnf.
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 .
10
Example
This example solves a complex sparse non-Hermitian linear system of equations using the CGS method, with incomplete preconditioning.
10.1
Program Text
10.2
Program Data
10.3
Program Results