NAG Library Function Document
nag_sparse_herm_chol_sol (f11jqc)
1
Purpose
nag_sparse_herm_chol_sol (f11jqc) solves a complex sparse Hermitian system of linear equations, represented in symmetric coordinate storage format, using a conjugate gradient or Lanczos method, with incomplete Cholesky preconditioning.
2
Specification
#include <nag.h> |
#include <nagf11.h> |
void |
nag_sparse_herm_chol_sol (Nag_SparseSym_Method method,
Integer n,
Integer nnz,
const Complex a[],
Integer la,
const Integer irow[],
const Integer icol[],
const Integer ipiv[],
const Integer istr[],
const Complex b[],
double tol,
Integer maxitn,
Complex x[],
double *rnorm,
Integer *itn,
NagError *fail) |
|
3
Description
nag_sparse_herm_chol_sol (f11jqc) solves a complex sparse Hermitian linear system of equations
using a preconditioned conjugate gradient method (see
Meijerink and Van der Vorst (1977)), 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).
nag_sparse_herm_chol_sol (f11jqc) uses the incomplete Cholesky factorization determined by
nag_sparse_herm_chol_fac (f11jnc) as the preconditioning matrix. A call to
nag_sparse_herm_chol_sol (f11jqc) must always be preceded by a call to
nag_sparse_herm_chol_fac (f11jnc). Alternative preconditioners for the same storage scheme are available by calling
nag_sparse_herm_sol (f11jsc).
The matrix
and the preconditioning matrix
are represented in symmetric coordinate storage (SCS) format (see
Section 2.1.2 in the f11 Chapter Introduction) in the arrays
a,
irow and
icol, as returned from
nag_sparse_herm_chol_fac (f11jnc). The array
a holds the nonzero entries in the lower triangular parts of these matrices, 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
Meijerink J and Van der Vorst H (1977) An iterative solution method for linear systems of which the coefficient matrix is a symmetric M-matrix Math. Comput. 31 148–162
Paige C C and Saunders M A (1975) Solution of sparse indefinite systems of linear equations SIAM J. Numer. Anal. 12 617–629
5
Arguments
- 1:
– Nag_SparseSym_MethodInput
-
On entry: specifies the iterative method to be used.
- Conjugate gradient method.
- Lanczos method (SYMMLQ).
Constraint:
or .
- 2:
– IntegerInput
-
On entry:
, the order of the matrix
. This
must be the same value as was supplied in the preceding call to
nag_sparse_herm_chol_fac (f11jnc).
Constraint:
.
- 3:
– IntegerInput
-
On entry: the number of nonzero elements in the lower triangular part of the matrix
. This
must be the same value as was supplied in the preceding call to
nag_sparse_herm_chol_fac (f11jnc).
Constraint:
.
- 4:
– const ComplexInput
-
On entry: the values returned in the array
a by a previous call to
nag_sparse_herm_chol_fac (f11jnc).
- 5:
– IntegerInput
-
On entry: the dimension of the arrays
a,
irow and
icol. This
must be the same value as was supplied in the preceding call to
nag_sparse_herm_chol_fac (f11jnc).
Constraint:
.
- 6:
– const IntegerInput
- 7:
– const IntegerInput
- 8:
– const IntegerInput
- 9:
– const IntegerInput
-
On entry: the values returned in arrays
irow,
icol,
ipiv and
istr by a previous call to
nag_sparse_herm_chol_fac (f11jnc).
- 10:
– const ComplexInput
-
On entry: the right-hand side vector .
- 11:
– doubleInput
-
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:
– IntegerInput
-
On entry: the maximum number of iterations allowed.
Constraint:
.
- 13:
– ComplexInput/Output
-
On entry: an initial approximation to the solution vector .
On exit: an improved approximation to the solution vector .
- 14:
– double *Output
-
On exit: the final value of the residual norm
, where
is the output value of
itn.
- 15:
– Integer *Output
-
On exit: the number of iterations carried out.
- 16:
– NagError *Input/Output
-
The NAG error argument (see
Section 3.7 in How to Use the NAG Library and its Documentation).
6
Error Indicators and Warnings
- NE_ACCURACY
-
The required accuracy could not be obtained. However a reasonable accuracy has been achieved.
- 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 had an illegal value.
- NE_COEFF_NOT_POS_DEF
-
The matrix of the coefficients
a appears not to be positive definite. The computation cannot continue.
- NE_CONVERGENCE
-
The solution has not converged after iterations.
- NE_INT
-
On entry, .
Constraint: .
On entry, .
Constraint: .
On entry, .
Constraint: .
- NE_INT_2
-
On entry, and .
Constraint: .
On entry, and .
Constraint: .
- 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.
A serious error, code , has occurred in an internal call. Check all function calls and array sizes. Seek expert help.
- NE_INVALID_SCS
-
On entry,
,
,
.
Constraint:
and
.
Check that
a,
irow,
icol,
ipiv and
istr have not been corrupted between calls to
nag_sparse_herm_chol_fac (f11jnc) and
nag_sparse_herm_chol_sol (f11jqc).
On entry,
,
and
.
Constraint:
and
.
Check that
a,
irow,
icol,
ipiv and
istr have not been corrupted between calls to
nag_sparse_herm_chol_fac (f11jnc) and
nag_sparse_herm_chol_sol (f11jqc).
- NE_INVALID_SCS_PRECOND
-
The SCS representation of the preconditioner is invalid. Check that
a,
irow,
icol,
ipiv and
istr have not been corrupted between calls to
nag_sparse_herm_chol_fac (f11jnc) and
nag_sparse_herm_chol_sol (f11jqc).
- 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,
is out of order:
. Check that
a,
irow,
icol,
ipiv and
istr have not been corrupted between calls to
nag_sparse_herm_chol_fac (f11jnc) and
nag_sparse_herm_chol_sol (f11jqc).
On entry, the location (
) is a duplicate:
. Check that
a,
irow,
icol,
ipiv and
istr have not been corrupted between calls to
nag_sparse_herm_chol_fac (f11jnc) and
nag_sparse_herm_chol_sol (f11jqc).
- NE_PRECOND_NOT_POS_DEF
-
The preconditioner appears not to be positive definite. The computation cannot continue.
- NE_REAL
-
On entry, .
Constraint: .
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
nag_sparse_herm_chol_sol (f11jqc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_sparse_herm_chol_sol (f11jqc) 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.
The time taken by
nag_sparse_herm_chol_sol (f11jqc) for each iteration is roughly proportional to the value of
nnzc returned from the preceding call to
nag_sparse_herm_chol_fac (f11jnc). 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 incomplete Cholesky preconditioning.
10.1
Program Text
Program Text (f11jqce.c)
10.2
Program Data
Program Data (f11jqce.d)
10.3
Program Results
Program Results (f11jqce.r)