Integer, Intent (In)	::	n, nrhs, ipiv(*), ldb, ldx
Integer, Intent (Out)	::	iwork(n), info
Real (Kind=nag_wp), Intent (In)	::	ap(), afp(), b(ldb,*)
Real (Kind=nag_wp), Intent (Inout)	::	x(ldx,*)
Real (Kind=nag_wp), Intent (Out)	::	ferr(nrhs), berr(nrhs), work(3*n)
Character (1), Intent (In)	::	uplo

C Header Interface

#include <nag.h>

void

f07phf_ (const char *uplo, const Integer *n, const Integer *nrhs, const double ap[], const double afp[], const Integer ipiv[], const double b[], const Integer *ldb, double x[], const Integer *ldx, double ferr[], double berr[], double work[], Integer iwork[], Integer *info, const Charlen length_uplo)

The routine may be called by the names f07phf, nagf_lapacklin_dsprfs or its LAPACK name dsprfs.

3 Description

f07phf returns the backward errors and estimated bounds on the forward errors for the solution of a real symmetric indefinite system of linear equations with multiple right-hand sides

A X = 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 f07phf 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

\begin{matrix} (A + δ A) x = b + δ b \\ | δ a_{i j} | \leq β | a_{i j} | and | δ b_{i} | \leq β | b_{i} | . \end{matrix}

Then the routine estimates a bound for the component-wise forward error in the computed solution, defined by:

\max_{i} | x_{i} - {\hat{x}}_{i} | / \max_{i} | x_{i} |

where

\hat{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 Arguments

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 $P U D U^{T} P^{T}$ , where $U$ is upper triangular.
$uplo ='L'$: The lower triangular part of $A$ is stored and $A$ is factorized as $P L D L^{T} P^{T}$ , where $L$ is lower triangular.

Constraint:

uplo ='U'

'L'

2: $n$ – Integer Input

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

3: $nrhs$ – Integer Input

On entry:

r

, the number of right-hand sides.

Constraint:

nrhs \geq 0

4: $ap (*)$ – Real (Kind=nag_wp) array Input

Note: the dimension of the array ap must be at least

\max (1, n \times (n + 1) / 2)

On entry: the

n \times n

original symmetric matrix

A

as supplied to f07pdf.

5: $afp (*)$ – Real (Kind=nag_wp) array Input

Note: the dimension of the array afp must be at least

\max (1, n \times (n + 1) / 2)

On entry: the factorization of

A

stored in packed form, as returned by f07pdf.

6: $ipiv (*)$ – Integer array Input

Note: the dimension of the array ipiv must be at least

\max (1, n)

On entry: details of the interchanges and the block structure of

D

, as returned by f07pdf.

7: $b (ldb, *)$ – Real (Kind=nag_wp) array Input

Note: the second dimension of the array b must be at least

\max (1, nrhs)

On entry: the

n \times r

right-hand side matrix

B

8: $ldb$ – Integer Input

On entry: the first dimension of the array b as declared in the (sub)program from which f07phf is called.

Constraint:

ldb \geq \max (1, n)

9: $x (ldx, *)$ – Real (Kind=nag_wp) array Input/Output

Note: the second dimension of the array x must be at least

\max (1, nrhs)

On entry: the

n \times r

solution matrix

X

, as returned by f07pef.

On exit: the improved solution matrix

X

10: $ldx$ – Integer Input

On entry: the first dimension of the array x as declared in the (sub)program from which f07phf is called.

Constraint:

ldx \geq \max (1, n)

11: $ferr (nrhs)$ – Real (Kind=nag_wp) array Output

On exit:

ferr (j)

contains an estimated error bound for the

j

th solution vector, that is, the

j

th column of

X

, for

j = 1, 2, \dots, r

12: $berr (nrhs)$ – Real (Kind=nag_wp) array Output

On exit:

berr (j)

contains the component-wise backward error bound

β

for the

j

th solution vector, that is, the

j

th column of

X

, for

j = 1, 2, \dots, r

13: $work (3 \times n)$ – Real (Kind=nag_wp) array Workspace

14: $iwork (n)$ – Integer array Workspace

15: $info$ – Integer Output

On exit:

info = 0

unless the routine detects an error (see Section 6).

6 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.

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 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

f07phf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f07phf 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.

9 Further Comments

For each right-hand side, computation of the backward error involves a minimum of

4 n^{2}

floating-point operations. Each step of iterative refinement involves an additional

6 n^{2}

operations. At most five steps of iterative refinement are performed, but usually only

1

2

steps are required.

Estimating the forward error involves solving a number of systems of linear equations of the form

A x = b

; the number is usually

4

5

and never more than

11

. Each solution involves approximately

2 n^{2}

operations.

The complex analogues of this routine are f07pvf for Hermitian matrices and f07qvf for symmetric matrices.

10 Example

This example solves the system of equations

A X = B

using iterative refinement and to compute the forward and backward error bounds, where

A = (\begin{matrix} 2.07 & 3.87 & 4.20 & - 1.15 \\ 3.87 & - 0.21 & 1.87 & 0.63 \\ 4.20 & 1.87 & 1.15 & 2.06 \\ - 1.15 & 0.63 & 2.06 & - 1.81 \end{matrix}) and B = (\begin{matrix} - 9.50 & 27.85 \\ - 8.38 & 9.90 \\ - 6.07 & 19.25 \\ - 0.96 & 3.93 \end{matrix}) .

Here

A

is symmetric indefinite, stored in packed form, and must first be factorized by f07pdf.

f07ph: FL CL CPP AD PY MB

NAG FL Interfacef07phf (dsprfs)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
f07phf (dsprfs)