sgebal - balance a general real matrix A
SUBROUTINE SGEBAL( JOB, N, A, LDA, ILO, IHI, SCALE, INFO) CHARACTER * 1 JOB INTEGER N, LDA, ILO, IHI, INFO REAL A(LDA,*), SCALE(*)
SUBROUTINE SGEBAL_64( JOB, N, A, LDA, ILO, IHI, SCALE, INFO) CHARACTER * 1 JOB INTEGER*8 N, LDA, ILO, IHI, INFO REAL A(LDA,*), SCALE(*)
SUBROUTINE GEBAL( JOB, [N], A, [LDA], ILO, IHI, SCALE, [INFO]) CHARACTER(LEN=1) :: JOB INTEGER :: N, LDA, ILO, IHI, INFO REAL, DIMENSION(:) :: SCALE REAL, DIMENSION(:,:) :: A
SUBROUTINE GEBAL_64( JOB, [N], A, [LDA], ILO, IHI, SCALE, [INFO]) CHARACTER(LEN=1) :: JOB INTEGER(8) :: N, LDA, ILO, IHI, INFO REAL, DIMENSION(:) :: SCALE REAL, DIMENSION(:,:) :: A
#include <sunperf.h>
void sgebal(char job, int n, float *a, int lda, int *ilo, int *ihi, float *scale, int *info);
void sgebal_64(char job, long n, float *a, long lda, long *ilo, long *ihi, float *scale, long *info);
sgebal balances a general real matrix A. This involves, first, permuting A by a similarity transformation to isolate eigenvalues in the first 1 to ILO-1 and last IHI+1 to N elements on the diagonal; and second, applying a diagonal similarity transformation to rows and columns ILO to IHI to make the rows and columns as close in norm as possible. Both steps are optional.
Balancing may reduce the 1-norm of the matrix, and improve the accuracy of the computed eigenvalues and/or eigenvectors.
= 'N': none: simply set ILO = 1, IHI = N, SCALE(I) = 1.0 for i = 1,...,N; = 'P': permute only;
= 'S': scale only;
= 'B': both permute and scale.
A(i,j)
= 0 if i > j and j = 1,...,ILO-1 or I = IHI+1,...,N.
If JOB = 'N' or 'S', ILO = 1 and IHI = N.
P(j)
is the index of the row and column interchanged
with row and column j and D(j)
is the scaling factor
applied to row and column j, then
SCALE(j)
= P(j)
for j = 1,...,ILO-1
= D(j)
for j = ILO,...,IHI
= P(j)
for j = IHI+1,...,N.
The order in which the interchanges are made is N to IHI+1,
then 1 to ILO-1.
= 0: successful exit.
< 0: if INFO = -i, the i-th argument had an illegal value.
The permutations consist of row and column interchanges which put the matrix in the form
( T1 X Y )
P A P = ( 0 B Z )
( 0 0 T2 )
where T1 and T2 are upper triangular matrices whose eigenvalues lie
along the diagonal. The column indices ILO and IHI mark the starting
and ending columns of the submatrix B. Balancing consists of applying
a diagonal similarity transformation inv(D)
* B * D to make the
1-norms of each row of B and its corresponding column nearly equal.
The output matrix is
( T1 X*D Y )
( 0 inv(D)*B*D inv(D)*Z ).
( 0 0 T2 )
Information about the permutations P and the diagonal matrix D is returned in the vector SCALE.
This subroutine is based on the EISPACK routine BALANC.
Modified by Tzu-Yi Chen, Computer Science Division, University of California at Berkeley, USA