NAG CL Interface
f04bjc (real_​symm_​packed_​solve)

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1 Purpose

f04bjc computes the solution to a real system of linear equations AX=B, where A is an n×n symmetric matrix, stored in packed format and X and B are n×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>
void  f04bjc (Nag_OrderType order, Nag_UploType uplo, Integer n, Integer nrhs, double ap[], Integer ipiv[], double b[], Integer pdb, double *rcond, double *errbnd, NagError *fail)
The function may be called by the names: f04bjc, nag_linsys_real_symm_packed_solve or nag_real_sym_packed_lin_solve.

3 Description

The diagonal pivoting method is used to factor A as A=UDUT, if uplo=Nag_Upper, or A=LDLT, if uplo=Nag_Lower, where U (or L) is a product of permutation and unit upper (lower) triangular matrices, and D is symmetric and block diagonal with 1×1 and 2×2 diagonal blocks. 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 https://www.netlib.org/lapack/lug
Higham N J (2002) Accuracy and Stability of Numerical Algorithms (2nd Edition) SIAM, Philadelphia

5 Arguments

1: order Nag_OrderType Input
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 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2: uplo Nag_UploType Input
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: n Integer Input
On entry: the number of linear equations n, i.e., the order of the matrix A.
Constraint: n0.
4: nrhs Integer Input
On entry: the number of right-hand sides r, i.e., the number of columns of the matrix B.
Constraint: nrhs0.
5: ap[dim] double Input/Output
Note: the dimension, dim, of the array ap must be at least max(1,n×(n+1)/2).
On entry: the n×n symmetric matrix A, packed column-wise in a linear array. The jth column of the matrix A is stored in the array ap as follows:
The storage of elements Aij depends on the order and uplo arguments as follows:
if order=Nag_ColMajor and uplo=Nag_Upper,
Aij is stored in ap[(j-1)×j/2+i-1], for ij;
if order=Nag_ColMajor and uplo=Nag_Lower,
Aij is stored in ap[(2n-j)×(j-1)/2+i-1], for ij;
if order=Nag_RowMajor and uplo=Nag_Upper,
Aij is stored in ap[(2n-i)×(i-1)/2+j-1], for ij;
if order=Nag_RowMajor and uplo=Nag_Lower,
Aij is stored in ap[(i-1)×i/2+j-1], for ij.
On exit: if fail.code= NE_NOERROR, the block diagonal matrix D and the multipliers used to obtain the factor U or L from the factorization A=UDUT or A=LDLT as computed by f07pdc, stored as a packed triangular matrix in the same storage format as A.
6: ipiv[n] Integer Output
On exit: if fail.code= NE_NOERROR, details of the interchanges and the block structure of D, as determined by f07pdc.
  • If ipiv[k-1]>0, then rows and columns k and ipiv[k-1] were interchanged, and dkk is a 1×1 diagonal block;
  • if uplo=Nag_Upper and ipiv[k-1]=ipiv[k-2]<0, then rows and columns k-1 and -ipiv[k-1] were interchanged and dk-1:k,k-1:k is a 2×2 diagonal block;
  • if uplo=Nag_Lower and ipiv[k-1]=ipiv[k]<0, then rows and columns k+1 and -ipiv[k-1] were interchanged and dk:k+1,k:k+1 is a 2×2 diagonal block.
7: b[dim] double Input/Output
Note: the dimension, dim, of the array b must be at least
  • max(1,pdb×nrhs) when order=Nag_ColMajor;
  • max(1,n×pdb) when order=Nag_RowMajor.
The (i,j)th 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×r matrix of right-hand sides B.
On exit: if fail.code= NE_NOERROR or NE_RCOND, the n×r solution matrix X.
8: pdb Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraints:
  • if order=Nag_ColMajor, pdbmax(1,n);
  • if order=Nag_RowMajor, pdbmax(1,nrhs).
9: rcond double * Output
On exit: if no constraints are violated, an estimate of the reciprocal of the condition number of the matrix A, computed as rcond=1/(A1A-11).
10: errbnd double * 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, errbnd is returned as unity.
11: fail NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
The Integer allocatable memory required is n, and the double allocatable memory required is 2×n. Allocation failed before the solution could be computed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
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, pdb=value.
Constraint: pdb>0.
NE_INT_2
On entry, pdb=value and n=value.
Constraint: pdbmax(1,n).
On entry, pdb=value and nrhs=value.
Constraint: pdbmax(1,nrhs).
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_RCOND
A solution has been computed, but rcond is less than machine precision so that the matrix A is numerically singular.
NE_SINGULAR
Diagonal block value of the block diagonal matrix is zero. The factorization has been completed, but the solution could not be computed.

7 Accuracy

The computed solution for a single right-hand side, x^, satisfies an equation of the form
(A+E) x^=b,  
where
E1 = O(ε) A1  
and ε is the machine precision. An approximate error bound for the computed solution is given by
x^-x1 x1 κ(A) E1 A1 ,  
where κ(A) = A-11 A1 , the condition number of A with respect to the solution of the linear equations. f04bjc uses the approximation E1=εA1 to estimate errbnd. See Section 4.4 of Anderson et al. (1999) for further details.

8 Parallelism and Performance

f04bjc 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The packed storage scheme is illustrated by the following example when n=4 and uplo=Nag_Upper. Two-dimensional storage of the symmetric matrix A:
a11 a12 a13 a14 a22 a23 a24 a33 a34 a44 (aij=aji)  
Packed storage of the upper triangle of A:
ap= [ a11, a12, a22, a13, a23, a33, a14, a24, a34, a44 ]  
The total number of floating-point operations required to solve the equations AX=B is proportional to (13n3+2n2r). 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 complex analogues of f04bjc are f04cjc for complex Hermitian matrices, and f04djc for complex symmetric matrices.

10 Example

This example solves the equations
AX=B,  
where A is the symmetric indefinite matrix
A= ( -1.81 2.06 0.63 -1.15 2.06 1.15 1.87 4.20 0.63 1.87 -0.21 3.87 -1.15 4.20 3.87 2.07 )   and   B= ( 0.96 3.93 6.07 19.25 8.38 9.90 9.50 27.85 ) .  
An estimate of the condition number of A and an approximate error bound for the computed solutions are also printed.

10.1 Program Text

Program Text (f04bjce.c)

10.2 Program Data

Program Data (f04bjce.d)

10.3 Program Results

Program Results (f04bjce.r)