NAG Library Routine Document
F11JSF
1 Purpose
F11JSF solves a complex sparse Hermitian system of linear equations, represented in symmetric coordinate storage format, using a conjugate gradient or Lanczos method, without preconditioning, with Jacobi or with SSOR preconditioning.
2 Specification
SUBROUTINE F11JSF ( |
METHOD, PRECON, N, NNZ, A, IROW, ICOL, OMEGA, B, TOL, MAXITN, X, RNORM, ITN, RDIAG, WORK, LWORK, IWORK, IFAIL) |
INTEGER |
N, NNZ, IROW(NNZ), ICOL(NNZ), MAXITN, ITN, LWORK, IWORK(N+1), IFAIL |
REAL (KIND=nag_wp) |
OMEGA, TOL, RNORM, RDIAG(N) |
COMPLEX (KIND=nag_wp) |
A(NNZ), B(N), X(N), WORK(LWORK) |
CHARACTER(*) |
METHOD |
CHARACTER(1) |
PRECON |
|
3 Description
F11JSF solves a complex sparse Hermitian linear system of equations
using a preconditioned conjugate gradient method (see
Barrett et al. (1994)), or a preconditioned Lanczos method based on the algorithm SYMMLQ (see
Paige and Saunders (1975)). The conjugate gradient method is more efficient if
is positive definite, but may fail to converge for indefinite matrices. In this case the Lanczos method should be used instead. For further details see
Barrett et al. (1994).
F11JSF 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 Cholesky (IC) preconditioning see
F11JQF.
The matrix
is represented in symmetric coordinate storage (SCS) format (see
Section 2.1.2 in the F11 Chapter Introduction) in the arrays
A,
IROW and
ICOL. The array
A holds the nonzero entries in the lower triangular part of the matrix, while
IROW and
ICOL hold the corresponding row and column indices.
4 References
Barrett R, Berry M, Chan T F, Demmel J, Donato J, Dongarra J, Eijkhout V, Pozo R, Romine C and Van der Vorst H (1994) Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods SIAM, Philadelphia
Paige C C and Saunders M A (1975) Solution of sparse indefinite systems of linear equations SIAM J. Numer. Anal. 12 617–629
Young D (1971) Iterative Solution of Large Linear Systems Academic Press, New York
5 Parameters
- 1: – CHARACTER(*)Input
-
On entry: specifies the iterative method to be used.
- Conjugate gradient method.
- Lanczos method (SYMMLQ).
Constraint:
or .
- 2: – CHARACTER(1)Input
-
On entry: specifies the type of preconditioning to be used.
- No preconditioning.
- Jacobi.
- Symmetric successive-over-relaxation (SSOR).
Constraint:
, or .
- 3: – INTEGERInput
-
On entry: , the order of the matrix .
Constraint:
.
- 4: – INTEGERInput
-
On entry: the number of nonzero elements in the lower triangular part of the matrix .
Constraint:
.
- 5: – COMPLEX (KIND=nag_wp) arrayInput
-
On entry: the nonzero elements of the lower triangular part 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
F11ZPF may be used to order the elements in this way.
- 6: – INTEGER arrayInput
- 7: – INTEGER arrayInput
-
On entry: the row and column indices of the nonzero elements supplied in array
A.
Constraints:
IROW and
ICOL must satisfy these constraints (which may be imposed by a call to
F11ZPF):
- and , for ;
- or and , for .
- 8: – 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.
Constraint:
.
- 9: – COMPLEX (KIND=nag_wp) arrayInput
-
On entry: the right-hand side vector .
- 10: – 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:
.
- 11: – INTEGERInput
-
On entry: the maximum number of iterations allowed.
Constraint:
.
- 12: – COMPLEX (KIND=nag_wp) arrayInput/Output
-
On entry: an initial approximation to the solution vector .
On exit: an improved approximation to the solution vector .
- 13: – REAL (KIND=nag_wp)Output
-
On exit: the final value of the residual norm
, where
is the output value of
ITN.
- 14: – INTEGEROutput
-
On exit: the number of iterations carried out.
- 15: – REAL (KIND=nag_wp) arrayOutput
-
On exit: the elements of the diagonal matrix , where is the diagonal part of . Note that since is Hermitian the elements of are necessarily real.
- 16: – COMPLEX (KIND=nag_wp) arrayWorkspace
- 17: – INTEGERInput
-
On entry: the dimension of the array
WORK as declared in the (sub)program from which F11JSF is called.
Constraints:
- if , ;
- if , .
- 18: – INTEGER arrayWorkspace
-
- 19: – 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 , |
or | , or , |
or | , |
or | , |
or | , |
or | OMEGA lies outside the interval , |
or | , |
or | , |
or | LWORK is 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 in the lower triangular part of
, is out of order, or has duplicate row and column indices. Call
F11ZPF 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 has been obtained and further iterations could not improve the result.
-
Required accuracy not obtained in
MAXITN iterations.
-
The preconditioner appears not to be positive definite.
-
The matrix of the coefficients appears not to be positive definite (conjugate gradient method only).
-
A serious error has occurred in an internal call to an auxiliary routine. Check all subroutine calls and array sizes. Seek expert help.
-
The matrix of the coefficients has a non-real diagonal entry, and is therefore not Hermitian.
An unexpected error has been triggered by this routine. Please
contact
NAG.
See
Section 3.8 in the Essential Introduction for further information.
Your licence key may have expired or may not have been installed correctly.
See
Section 3.7 in the Essential Introduction for further information.
Dynamic memory allocation failed.
See
Section 3.6 in the Essential Introduction 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
F11JSF is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
F11JSF 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 F11JSF for each iteration is roughly proportional to
NNZ. One iteration with the Lanczos method (SYMMLQ) requires a slightly larger number of operations than one iteration with the conjugate gradient method.
The number of iterations required to achieve a prescribed accuracy cannot easily be determined a priori, as it can depend dramatically on the conditioning and spectrum of the preconditioned matrix of the coefficients .
10 Example
This example solves a complex sparse Hermitian positive definite system of equations using the conjugate gradient method, with SSOR preconditioning.
10.1 Program Text
Program Text (f11jsfe.f90)
10.2 Program Data
Program Data (f11jsfe.d)
10.3 Program Results
Program Results (f11jsfe.r)