CXML
SBDSQR (3lapack)
compute the singular value decomposition (SVD) of a real N-by-N
(upper or lower) bidiagonal matrix B
SYNOPSIS
SUBROUTINE SBDSQR( UPLO, N, NCVT, NRU, NCC, D, E, VT, LDVT, U, LDU, C, LDC,
WORK, INFO )
CHARACTER UPLO
INTEGER INFO, LDC, LDU, LDVT, N, NCC, NCVT, NRU
REAL C( LDC, * ), D( * ), E( * ), U( LDU, * ), VT( LDVT, * ),
WORK( * )
PURPOSE
SBDSQR computes the singular value decomposition (SVD) of a real N-by-N
(upper or lower) bidiagonal matrix B: B = Q * S * P' (P' denotes the
transpose of P), where S is a diagonal matrix with non-negative diagonal
elements (the singular values of B), and Q and P are orthogonal matrices.
The routine computes S, and optionally computes U * Q, P' * VT, or Q' * C,
for given real input matrices U, VT, and C.
See "Computing Small Singular Values of Bidiagonal Matrices With
Guaranteed High Relative Accuracy," by J. Demmel and W. Kahan, LAPACK
Working Note #3 (or SIAM J. Sci. Statist. Comput. vol. 11, no. 5, pp. 873-
912, Sept 1990) and
"Accurate singular values and differential qd algorithms," by B. Parlett
and V. Fernando, Technical Report CPAM-554, Mathematics Department,
University of California at Berkeley, July 1992 for a detailed description
of the algorithm.
ARGUMENTS
UPLO (input) CHARACTER*1
= 'U': B is upper bidiagonal;
= 'L': B is lower bidiagonal.
N (input) INTEGER
The order of the matrix B. N >= 0.
NCVT (input) INTEGER
The number of columns of the matrix VT. NCVT >= 0.
NRU (input) INTEGER
The number of rows of the matrix U. NRU >= 0.
NCC (input) INTEGER
The number of columns of the matrix C. NCC >= 0.
D (input/output) REAL array, dimension (N)
On entry, the n diagonal elements of the bidiagonal matrix B. On
exit, if INFO=0, the singular values of B in decreasing order.
E (input/output) REAL array, dimension (N)
On entry, the elements of E contain the offdiagonal elements of the
bidiagonal matrix whose SVD is desired. On normal exit (INFO = 0),
E is destroyed. If the algorithm does not converge (INFO > 0), D
and E will contain the diagonal and superdiagonal elements of a
bidiagonal matrix orthogonally equivalent to the one given as
input. E(N) is used for workspace.
VT (input/output) REAL array, dimension (LDVT, NCVT)
On entry, an N-by-NCVT matrix VT. On exit, VT is overwritten by P'
* VT. VT is not referenced if NCVT = 0.
LDVT (input) INTEGER
The leading dimension of the array VT. LDVT >= max(1,N) if NCVT >
0; LDVT >= 1 if NCVT = 0.
U (input/output) REAL array, dimension (LDU, N)
On entry, an NRU-by-N matrix U. On exit, U is overwritten by U *
Q. U is not referenced if NRU = 0.
LDU (input) INTEGER
The leading dimension of the array U. LDU >= max(1,NRU).
C (input/output) REAL array, dimension (LDC, NCC)
On entry, an N-by-NCC matrix C. On exit, C is overwritten by Q' *
C. C is not referenced if NCC = 0.
LDC (input) INTEGER
The leading dimension of the array C. LDC >= max(1,N) if NCC > 0;
LDC >=1 if NCC = 0.
WORK (workspace) REAL array, dimension
2*N if only singular values wanted (NCVT = NRU = NCC = 0) max( 1,
4*N-4 ) otherwise
INFO (output) INTEGER
= 0: successful exit
< 0: If INFO = -i, the i-th argument had an illegal value
> 0: the algorithm did not converge; D and E contain the elements
of a bidiagonal matrix which is orthogonally similar to the input
matrix B; if INFO = i, i elements of E have not converged to zero.
PARAMETERS
TOLMUL REAL, default = max(10,min(100,EPS**(-1/8)))
TOLMUL controls the convergence criterion of the QR loop. If it is
positive, TOLMUL*EPS is the desired relative precision in the
computed singular values. If it is negative,
abs(TOLMUL*EPS*sigma_max) is the desired absolute accuracy in the
computed singular values (corresponds to relative accuracy
abs(TOLMUL*EPS) in the largest singular value. abs(TOLMUL) should
be between 1 and 1/EPS, and preferably between 10 (for fast
convergence) and .1/EPS (for there to be some accuracy in the
results). Default is to lose at either one eighth or 2 of the
available decimal digits in each computed singular value (whichever
is smaller).
MAXITR INTEGER, default = 6
MAXITR controls the maximum number of passes of the algorithm
through its inner loop. The algorithms stops (and so fails to
converge) if the number of passes through the inner loop exceeds
MAXITR*N**2.
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