F07GVF (ZPPRFS) (PDF version)
F07 Chapter Contents
F07 Chapter Introduction
NAG Library Manual

NAG Library Routine Document

F07GVF (ZPPRFS)

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

F07GVF (ZPPRFS) returns error bounds for the solution of a complex Hermitian positive definite system of linear equations with multiple right-hand sides, AX=B, using packed storage. It improves the solution by iterative refinement, in order to reduce the backward error as much as possible.

2  Specification

SUBROUTINE F07GVF ( UPLO, N, NRHS, AP, AFP, B, LDB, X, LDX, FERR, BERR, WORK, RWORK, INFO)
INTEGER  N, NRHS, LDB, LDX, INFO
REAL (KIND=nag_wp)  FERR(NRHS), BERR(NRHS), RWORK(N)
COMPLEX (KIND=nag_wp)  AP(*), AFP(*), B(LDB,*), X(LDX,*), WORK(2*N)
CHARACTER(1)  UPLO
The routine may be called by its LAPACK name zpprfs.

3  Description

F07GVF (ZPPRFS) returns the backward errors and estimated bounds on the forward errors for the solution of a complex Hermitian positive definite system of linear equations with multiple right-hand sides AX=B, using packed storage. The routine handles each right-hand side vector (stored as a column of the matrix B) independently, so we describe the function of F07GVF (ZPPRFS) in terms of a single right-hand side b and solution x.
Given a computed solution x, the routine 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 routine 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.

4  References

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

5  Parameters

1:     UPLO – CHARACTER(1)Input
On entry: 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 UHU, where U is upper triangular.
UPLO='L'
The lower triangular part of A is stored and A is factorized as LLH, where L is lower triangular.
Constraint: UPLO='U' or 'L'.
2:     N – INTEGERInput
On entry: n, the order of the matrix A.
Constraint: N0.
3:     NRHS – INTEGERInput
On entry: r, the number of right-hand sides.
Constraint: NRHS0.
4:     AP(*) – COMPLEX (KIND=nag_wp) arrayInput
Note: the dimension of the array AP must be at least max1,N×N+1/2.
On entry: the n by n original Hermitian positive definite matrix A as supplied to F07GRF (ZPPTRF).
5:     AFP(*) – COMPLEX (KIND=nag_wp) arrayInput
Note: the dimension of the array AFP must be at least max1,N×N+1/2.
On entry: the Cholesky factor of A stored in packed form, as returned by F07GRF (ZPPTRF).
6:     B(LDB,*) – COMPLEX (KIND=nag_wp) arrayInput
Note: the second dimension of the array B must be at least max1,NRHS.
On entry: the n by r right-hand side matrix B.
7:     LDB – INTEGERInput
On entry: the first dimension of the array B as declared in the (sub)program from which F07GVF (ZPPRFS) is called.
Constraint: LDBmax1,N.
8:     X(LDX,*) – COMPLEX (KIND=nag_wp) arrayInput/Output
Note: the second dimension of the array X must be at least max1,NRHS.
On entry: the n by r solution matrix X, as returned by F07GSF (ZPPTRS).
On exit: the improved solution matrix X.
9:     LDX – INTEGERInput
On entry: the first dimension of the array X as declared in the (sub)program from which F07GVF (ZPPRFS) is called.
Constraint: LDXmax1,N.
10:   FERR(NRHS) – REAL (KIND=nag_wp) arrayOutput
On exit: FERRj contains an estimated error bound for the jth solution vector, that is, the jth column of X, for j=1,2,,r.
11:   BERR(NRHS) – REAL (KIND=nag_wp) arrayOutput
On exit: 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.
12:   WORK(2×N) – COMPLEX (KIND=nag_wp) arrayWorkspace
13:   RWORK(N) – REAL (KIND=nag_wp) arrayWorkspace
14:   INFO – INTEGEROutput
On exit: INFO=0 unless the routine detects an error (see Section 6).

6  Error Indicators and Warnings

Errors or warnings detected by the routine:
INFO<0
If INFO=-i, the ith parameter had an illegal value. An explanatory message is output, and execution of the program is terminated.

7  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.

8  Further Comments

For each right-hand side, computation of the backward error involves a minimum of 16n2 real floating point operations. Each step of iterative refinement involves an additional 24n2 real 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 5 and never more than 11. Each solution involves approximately 8n2 real operations.
The real analogue of this routine is F07GHF (DPPRFS).

9  Example

This example solves the system of equations AX=B using iterative refinement and to compute the forward and backward error bounds, where
A= 3.23+0.00i 1.51-1.92i 1.90+0.84i 0.42+2.50i 1.51+1.92i 3.58+0.00i -0.23+1.11i -1.18+1.37i 1.90-0.84i -0.23-1.11i 4.09+0.00i 2.33-0.14i 0.42-2.50i -1.18-1.37i 2.33+0.14i 4.29+0.00i
and
B= 3.93-06.14i 1.48+06.58i 6.17+09.42i 4.65-04.75i -7.17-21.83i -4.91+02.29i 1.99-14.38i 7.64-10.79i .
Here A is Hermitian positive definite, stored in packed form, and must first be factorized by F07GRF (ZPPTRF).

9.1  Program Text

Program Text (f07gvfe.f90)

9.2  Program Data

Program Data (f07gvfe.d)

9.3  Program Results

Program Results (f07gvfe.r)


F07GVF (ZPPRFS) (PDF version)
F07 Chapter Contents
F07 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012