nag_herm_posdef_lin_solve (f04cdc) (PDF version)
f04 Chapter Contents
f04 Chapter Introduction
NAG C Library Manual

NAG Library Function Document

nag_herm_posdef_lin_solve (f04cdc)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_herm_posdef_lin_solve (f04cdc) computes the solution to a complex system of linear equations AX=B, where A is an n by n Hermitian positive definite matrix and X and B are n by r matrices. An estimate of the condition number of A and an error bound for the computed solution are also returned.

2  Specification

#include <nag.h>
#include <nagf04.h>
void  nag_herm_posdef_lin_solve (Nag_OrderType order, Nag_UploType uplo, Integer n, Integer nrhs, Complex a[], Integer pda, Complex b[], Integer pdb, double *rcond, double *errbnd, NagError *fail)

3  Description

The Cholesky factorization is used to factor A as A=UHU, if uplo=Nag_Upper, or A=LLH, if uplo=Nag_Lower, where U is an upper triangular matrix and L is a lower triangular matrix. The factored form of A is then used to solve the system of equations AX=B.

4  References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia http://www.netlib.org/lapack/lug
Higham N J (2002) Accuracy and Stability of Numerical Algorithms (2nd Edition) SIAM, Philadelphia

5  Arguments

1:     orderNag_OrderTypeInput
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2:     uploNag_UploTypeInput
On entry: if uplo=Nag_Upper, the upper triangle of the matrix A is stored.
If uplo=Nag_Lower, the lower triangle of the matrix A is stored.
Constraint: uplo=Nag_Upper or Nag_Lower.
3:     nIntegerInput
On entry: the number of linear equations n, i.e., the order of the matrix A.
Constraint: n0.
4:     nrhsIntegerInput
On entry: the number of right-hand sides r, i.e., the number of columns of the matrix B.
Constraint: nrhs0.
5:     a[dim]ComplexInput/Output
Note: the dimension, dim, of the array a must be at least max1,pda×n.
The i,jth element of the matrix A is stored in
  • a[j-1×pda+i-1] when order=Nag_ColMajor;
  • a[i-1×pda+j-1] when order=Nag_RowMajor.
On entry: the n by n Hermitian matrix A.
If uplo=Nag_Upper, the leading n by n upper triangular part of a contains the upper triangular part of the matrix A, and the strictly lower triangular part of a is not referenced.
If uplo=Nag_Lower, the leading n by n lower triangular part of a contains the lower triangular part of the matrix A, and the strictly upper triangular part of a is not referenced.
On exit: if fail.code= NE_NOERROR or NE_RCOND, the factor U or L from the Cholesky factorization A=UHU or A=LLH.
6:     pdaIntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array a.
Constraint: pdamax1,n.
7:     b[dim]ComplexInput/Output
Note: the dimension, dim, of the array b must be at least
  • max1,pdb×nrhs when order=Nag_ColMajor;
  • max1,n×pdb when order=Nag_RowMajor.
The i,jth element of the matrix B is stored in
  • b[j-1×pdb+i-1] when order=Nag_ColMajor;
  • b[i-1×pdb+j-1] when order=Nag_RowMajor.
On entry: the n by r matrix of right-hand sides B.
On exit: if fail.code= NE_NOERROR or NE_RCOND, the n by r solution matrix X.
8:     pdbIntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraints:
  • if order=Nag_ColMajor, pdbmax1,n;
  • if order=Nag_RowMajor, pdbmax1,nrhs.
9:     rconddouble *Output
On exit: if fail.code= NE_NOERROR or NE_RCOND, an estimate of the reciprocal of the condition number of the matrix A, computed as rcond=1/A1A-11.
10:   errbnddouble *Output
On exit: if fail.code= NE_NOERROR or NE_RCOND, an estimate of the forward error bound for a computed solution x^, such that x^-x1/x1errbnd, where x^ is a column of the computed solution returned in the array b and x is the corresponding column of the exact solution X. If rcond is less than machine precision, then errbnd is returned as unity.
11:   failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, n=value.
Constraint: n0.
On entry, nrhs=value.
Constraint: nrhs0.
On entry, pda=value.
Constraint: pda>0.
On entry, pdb=value.
Constraint: pdb>0.
NE_INT_2
On entry, pda=value and n=value.
Constraint: pdamax1,n.
On entry, pdb =value and n =value.
Constraint: pdbmax1,n.
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_POS_DEF
The principal minor of order value of the matrix A is not positive definite. The factorization has not been completed and the solution could not be computed.
NE_RCOND
A solution has been computed, but rcond is less than machine precision so that the matrix A is numerically singular.

7  Accuracy

The computed solution for a single right-hand side, x^, satisfies an equation of the form
A+E x^=b,
where
E1 = Oε A1
and ε is the machine precision. An approximate error bound for the computed solution is given by
x^-x1 x1 κA E1 A1 ,
where κA = A-11 A1 , the condition number of A with respect to the solution of the linear equations. nag_herm_posdef_lin_solve (f04cdc) uses the approximation E1=εA1 to estimate errbnd. See Section 4.4 of Anderson et al. (1999) for further details.

8  Further Comments

The total number of floating point operations required to solve the equations AX=B is proportional to 13n3+n2r. The condition number estimation typically requires between four and five solves and never more than eleven solves, following the factorization.
In practice the condition number estimator is very reliable, but it can underestimate the true condition number; see Section 15.3 of Higham (2002) for further details.
The real analogue of nag_herm_posdef_lin_solve (f04cdc) is nag_real_sym_posdef_lin_solve (f04bdc).

9  Example

This example solves the equations
AX=B,
where A is the Hermitian positive definite matrix
A= 3.23i+0.00 1.51-1.92i 1.90+0.84i 0.42+2.50i 1.51+1.92i 3.58i+0.00 -0.23+1.11i -1.18+1.37i 1.90-0.84i -0.23-1.11i 4.09i+0.00 2.33-0.14i 0.42-2.50i -1.18-1.37i 2.33+0.14i 4.29i+0.00
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 .
An estimate of the condition number of A and an approximate error bound for the computed solutions are also printed.

9.1  Program Text

Program Text (f04cdce.c)

9.2  Program Data

Program Data (f04cdce.d)

9.3  Program Results

Program Results (f04cdce.r)


nag_herm_posdef_lin_solve (f04cdc) (PDF version)
f04 Chapter Contents
f04 Chapter Introduction
NAG C Library Manual

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