NAG Library Function Document
nag_sparse_sym_chol_sol (f11jcc)
1
Purpose
nag_sparse_sym_chol_sol (f11jcc) solves a real sparse symmetric 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_sym_chol_sol (Nag_SparseSym_Method method,
Integer n,
Integer nnz,
const double a[],
Integer la,
const Integer irow[],
const Integer icol[],
const Integer ipiv[],
const Integer istr[],
const double b[],
double tol,
Integer maxitn,
double x[],
double *rnorm,
Integer *itn,
Nag_Sparse_Comm *comm,
NagError *fail) |
|
3
Description
nag_sparse_sym_chol_sol (f11jcc) solves a real sparse symmetric linear system of equations:
using a preconditioned conjugate gradient method (
Meijerink and Van der Vorst (1977)), or a preconditioned Lanczos method based on the algorithm SYMMLQ (
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_sym_chol_sol (f11jcc) uses the incomplete Cholesky factorization determined by
nag_sparse_sym_chol_fac (f11jac) as the preconditioning matrix. A call to
nag_sparse_sym_chol_sol (f11jcc) must always be preceded by a call to
nag_sparse_sym_chol_fac (f11jac). Alternative preconditioners for the same storage scheme are available by calling
nag_sparse_sym_sol (f11jec).
The matrix
, and the preconditioning matrix
, are represented in symmetric coordinate storage (SCS) format (see the
f11 Chapter Introduction) in the arrays
a,
irow and
icol, as returned from
nag_sparse_sym_chol_fac (f11jac). 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
Salvini S A and Shaw G J (1995) An evaluation of new NAG Library solvers for large sparse symmetric linear systems NAG Technical Report TR1/95
5
Arguments
- 1:
– Nag_SparseSym_MethodInput
-
On entry: specifies the iterative method to be used.
- The conjugate gradient method is used.
- The Lanczos method, SYMMLQ is used.
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_sym_chol_fac (f11jac).
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_sym_chol_fac (f11jac).
Constraint:
.
- 4:
– const doubleInput
-
On entry: the values returned in array
a by a previous call to
nag_sparse_sym_chol_fac (f11jac).
- 5:
– IntegerInput
-
On entry: the
second
dimension of the arrays
a,
irow and
icol.This
must be the same value as returned by a previous call to
nag_sparse_sym_chol_fac (f11jac).
Constraint:
.
- 6:
– const IntegerInput
- 7:
– const IntegerInput
- 8:
– const IntegerInput
- 9:
– const IntegerInput
-
On entry: the values returned in the arrays
irow,
icol,
ipiv and
istr by a previous call to
nag_sparse_sym_chol_fac (f11jac).
- 10:
– const doubleInput
-
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:
– doubleInput/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:
– Nag_Sparse_Comm *Input/Output
-
On entry/exit: a pointer to a structure of type Nag_Sparse_Comm whose members are used by the iterative solver.
- 17:
– 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_2_INT_ARG_LT
-
On entry, while . These arguments must satisfy .
- NE_ACC_LIMIT
-
The required accuracy could not be obtained. However, a reasonable accuracy has been obtained and further iterations cannot improve the result.
- NE_ALLOC_FAIL
-
Dynamic memory allocation failed.
- NE_BAD_PARAM
-
On entry, argument
method had an illegal value.
- NE_COEFF_NOT_POS_DEF
-
The matrix of coefficients appears not to be positive definite.
- NE_INT_2
-
On entry, , .
Constraint: .
- NE_INT_ARG_LT
-
On entry, .
Constraint: .
On entry, .
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.
- NE_INVALID_SCS
-
The SCS representation of the matrix
is invalid. Check that the call to
nag_sparse_sym_chol_sol (f11jcc) has been preceded by a valid call to
nag_sparse_sym_chol_fac (f11jac), and that the arrays
a,
irow and
icol have not been corrupted between the two calls.
- NE_INVALID_SCS_PRECOND
-
The SCS representation of the preconditioning matrix
is invalid. Check that the call to
nag_sparse_sym_chol_sol (f11jcc) has been preceded by a valid call to
nag_sparse_sym_chol_fac (f11jac), and that the arrays
a,
irow,
icol,
ipiv and
istr have not been corrupted between the two calls.
- NE_NOT_REQ_ACC
-
The required accuracy has not been obtained in
maxitn iterations.
- NE_PRECOND_NOT_POS_DEF
-
The preconditioner appears not to be positive definite.
- NE_REAL_ARG_GE
-
On entry,
tol must not be greater than or equal to 1.0:
.
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_sym_chol_sol (f11jcc) is not threaded in any implementation.
The time taken by
nag_sparse_sym_chol_sol (f11jcc) for each iteration is roughly proportional to the value of
nnzc returned from the preceding call to
nag_sparse_sym_chol_fac (f11jac). 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 be easily determined a priori, as it can depend dramatically on the conditioning and spectrum of the preconditioned matrix of the coefficients .
Some illustrations of the application of
nag_sparse_sym_chol_sol (f11jcc) to linear systems arising from the discretization of two-dimensional elliptic partial differential equations, and to random-valued randomly structured symmetric positive definite linear systems, can be found in
Salvini and Shaw (1995).
10
Example
This example program solves a symmetric positive definite system of equations using the conjugate gradient method, with incomplete Cholesky preconditioning.
10.1
Program Text
Program Text (f11jcce.c)
10.2
Program Data
Program Data (f11jcce.d)
10.3
Program Results
Program Results (f11jcce.r)