NAG Library Routine Document

f04asf  (withdraw_real_posdef_solve_1rhs)

 Contents

    1  Purpose
    7  Accuracy

1
Purpose

f04asf calculates the accurate solution of a set of real symmetric positive definite linear equations with a single right-hand side, Ax=b, using a Cholesky factorization and iterative refinement.

2
Specification

Fortran Interface
Subroutine f04asf ( a, lda, b, n, c, wk1, wk2, ifail)
Integer, Intent (In):: lda, n
Integer, Intent (Inout):: ifail
Real (Kind=nag_wp), Intent (In):: b(max(1,n))
Real (Kind=nag_wp), Intent (Inout):: a(lda,*)
Real (Kind=nag_wp), Intent (Out):: c(max(1,n)), wk1(max(1,n)), wk2(max(1,n))
C Header Interface
#include nagmk26.h
void  f04asf_ ( double a[], const Integer *lda, const double b[], const Integer *n, double c[], double wk1[], double wk2[], Integer *ifail)

3
Description

Given a set of real linear equations Ax=b, where A is a symmetric positive definite matrix, f04asf first computes a Cholesky factorization of A as A=LLT where L is lower triangular. An approximation to x is found by forward and backward substitution. The residual vector r=b-Ax is then calculated using additional precision and a correction d to x is found by solving LLTd=r. x is then replaced by x+d, and this iterative refinement of the solution is repeated until machine accuracy is obtained.

4
References

Wilkinson J H and Reinsch C (1971) Handbook for Automatic Computation II, Linear Algebra Springer–Verlag

5
Arguments

1:     alda* – Real (Kind=nag_wp) arrayInput/Output
Note: the second dimension of the array a must be at least max1,n.
On entry: the upper triangle of the n by n positive definite symmetric matrix A. The elements of the array below the diagonal need not be set.
On exit: the elements of the array below the diagonal are overwritten; the upper triangle of a is unchanged.
2:     lda – IntegerInput
On entry: the first dimension of the array a as declared in the (sub)program from which f04asf is called.
Constraint: ldamax1,n.
3:     bmax1,n – Real (Kind=nag_wp) arrayInput
Note: the dimension of the array b must be at least max1,n.
On entry: the right-hand side vector b.
4:     n – IntegerInput
On entry: n, the order of the matrix A.
Constraint: n0.
5:     cmax1,n – Real (Kind=nag_wp) arrayOutput
On exit: the solution vector x.
6:     wk1max1,n – Real (Kind=nag_wp) arrayWorkspace
7:     wk2max1,n – Real (Kind=nag_wp) arrayWorkspace
8:     ifail – IntegerInput/Output
On entry: ifail must be set to 0, -1​ or ​1. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value -1​ or ​1 is recommended. If the output of error messages is undesirable, then the value 1 is recommended. Otherwise, if you are not familiar with this argument, the recommended value is 0. When the value -1​ or ​1 is used it is essential to test the value of ifail on exit.
On exit: ifail=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6
Error Indicators and Warnings

If on entry ifail=0 or -1, explanatory error messages are output on the current error message unit (as defined by x04aaf).
Errors or warnings detected by the routine:
ifail=1
The matrix A is not positive definite, possibly due to rounding errors.
ifail=2
Iterative refinement fails to improve the solution, i.e., the matrix A is too ill-conditioned.
ifail=3
On entry,n<0,
orlda<max1,n.
ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.
ifail=-399
Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.
ifail=-999
Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7
Accuracy

The computed solutions should be correct to full machine accuracy. For a detailed error analysis see page 39 of Wilkinson and Reinsch (1971).

8
Parallelism and Performance

f04asf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f04asf 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

The time taken by f04asf is approximately proportional to n3.
The routine must not be called with the same name for arguments b and c.

10
Example

This example solves the set of linear equations Ax=b where
A= 5 7 6 5 7 10 8 7 6 8 10 9 5 7 9 10   and  b= 23 32 33 31 .  

10.1
Program Text

Program Text (f04asfe.f90)

10.2
Program Data

Program Data (f04asfe.d)

10.3
Program Results

Program Results (f04asfe.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017