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Chapter Introduction
NAG Toolbox

NAG Toolbox: nag_lapack_dporfs (f07fh)

 Contents

    1  Purpose
    2  Syntax
    7  Accuracy
    9  Example

Purpose

nag_lapack_dporfs (f07fh) returns error bounds for the solution of a real symmetric positive definite system of linear equations with multiple right-hand sides, AX=B. It improves the solution by iterative refinement, in order to reduce the backward error as much as possible.

Syntax

[x, ferr, berr, info] = f07fh(uplo, a, af, b, x, 'n', n, 'nrhs_p', nrhs_p)
[x, ferr, berr, info] = nag_lapack_dporfs(uplo, a, af, b, x, 'n', n, 'nrhs_p', nrhs_p)

Description

nag_lapack_dporfs (f07fh) returns the backward errors and estimated bounds on the forward errors for the solution of a real symmetric positive definite system of linear equations with multiple right-hand sides AX=B. The function handles each right-hand side vector (stored as a column of the matrix B) independently, so we describe the function of nag_lapack_dporfs (f07fh) in terms of a single right-hand side b and solution x.
Given a computed solution x, the function computes the component-wise backward error β. This is the size of the smallest relative perturbation in each element of A and b such that x is the exact solution of a perturbed system
A+δAx=b+δb δaijβaij   and   δbiβbi .  
Then the function estimates a bound for the component-wise forward error in the computed solution, defined by:
maxixi-x^i/maxixi  
where x^ is the true solution.
For details of the method, see the F07 Chapter Introduction.

References

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

Parameters

Compulsory Input Parameters

1:     uplo – string (length ≥ 1)
Specifies whether the upper or lower triangular part of A is stored and how A is to be factorized.
uplo='U'
The upper triangular part of A is stored and A is factorized as UTU, where U is upper triangular.
uplo='L'
The lower triangular part of A is stored and A is factorized as LLT, where L is lower triangular.
Constraint: uplo='U' or 'L'.
2:     alda: – double array
The first dimension of the array a must be at least max1,n.
The second dimension of the array a must be at least max1,n.
The n by n original symmetric positive definite matrix A as supplied to nag_lapack_dpotrf (f07fd).
3:     afldaf: – double array
The first dimension of the array af must be at least max1,n.
The second dimension of the array af must be at least max1,n.
The Cholesky factor of A, as returned by nag_lapack_dpotrf (f07fd).
4:     bldb: – double array
The first dimension of the array b must be at least max1,n.
The second dimension of the array b must be at least max1,nrhs_p.
The n by r right-hand side matrix B.
5:     xldx: – double array
The first dimension of the array x must be at least max1,n.
The second dimension of the array x must be at least max1,nrhs_p.
The n by r solution matrix X, as returned by nag_lapack_dpotrs (f07fe).

Optional Input Parameters

1:     n int64int32nag_int scalar
Default: the first dimension of the arrays a, af, b, x and the second dimension of the arrays a, af.
n, the order of the matrix A.
Constraint: n0.
2:     nrhs_p int64int32nag_int scalar
Default: the second dimension of the arrays b, x. (An error is raised if these dimensions are not equal.)
r, the number of right-hand sides.
Constraint: nrhs_p0.

Output Parameters

1:     xldx: – double array
The first dimension of the array x will be max1,n.
The second dimension of the array x will be max1,nrhs_p.
The improved solution matrix X.
2:     ferrnrhs_p – double array
ferrj contains an estimated error bound for the jth solution vector, that is, the jth column of X, for j=1,2,,r.
3:     berrnrhs_p – double array
berrj contains the component-wise backward error bound β for the jth solution vector, that is, the jth column of X, for j=1,2,,r.
4:     info int64int32nag_int scalar
info=0 unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

   info<0
If info=-i, argument i had an illegal value. An explanatory message is output, and execution of the program is terminated.

Accuracy

The bounds returned in ferr are not rigorous, because they are estimated, not computed exactly; but in practice they almost always overestimate the actual error.

Further Comments

For each right-hand side, computation of the backward error involves a minimum of 4n2 floating-point operations. Each step of iterative refinement involves an additional 6n2 operations. At most five steps of iterative refinement are performed, but usually only one or two steps are required.
Estimating the forward error involves solving a number of systems of linear equations of the form Ax=b; the number is usually 4 or 5 and never more than 11. Each solution involves approximately 2n2 operations.
The complex analogue of this function is nag_lapack_zporfs (f07fv).

Example

This example solves the system of equations AX=B using iterative refinement and to compute the forward and backward error bounds, where
A= 4.16 -3.12 0.56 -0.10 -3.12 5.03 -0.83 1.18 0.56 -0.83 0.76 0.34 -0.10 1.18 0.34 1.18   and   B= 8.70 8.30 -13.35 2.13 1.89 1.61 -4.14 5.00 .  
Here A is symmetric positive definite and must first be factorized by nag_lapack_dpotrf (f07fd).
function f07fh_example


fprintf('f07fh example results\n\n');

% Lower triangular part of symmetric matrix A
uplo = 'Lower';
a = [ 4.16,  0,    0,    0;
     -3.12,  5.03, 0,    0;
      0.56, -0.83, 0.76, 0;
     -0.10,  1.18, 0.34, 1.18];

[L, info] = f07fd( ...
                   uplo, a);

% Rhs
b = [  8.70, 8.30;
     -13.35, 2.13;
       1.89, 1.61;
      -4.14, 5.00];

% Solve
[x, info]  = f07fe( ...
                    uplo, L, b);

% Refine
[x, ferr, berr, info] = f07fh( ...
                               uplo, a, L, b, x);

[ifail] = x04ca( ...
                 'General', 'N', x, 'Solution(s)');

fprintf('\nBackward errors (machine-dependent)\n   ')
fprintf('%11.1e', berr);
fprintf('\nEstimated forward error bounds (machine-dependent)\n   ')
fprintf('%11.1e', ferr);
fprintf('\n');


f07fh example results

 Solution(s)
             1          2
 1      1.0000     4.0000
 2     -1.0000     3.0000
 3      2.0000     2.0000
 4     -3.0000     1.0000

Backward errors (machine-dependent)
       1.0e-16    5.0e-17
Estimated forward error bounds (machine-dependent)
       2.3e-14    2.3e-14

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