STGSJA(l) LAPACK routine (version 1.1) STGSJA(l)
NAME
STGSJA - compute the generalized singular value decomposition (GSVD) of two
real upper ``triangular (or trapezoidal)'' matrices A and B
SYNOPSIS
SUBROUTINE STGSJA( JOBU, JOBV, JOBQ, M, P, N, K, L, A, LDA, B, LDB, TOLA,
TOLB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK, NCYCLE,
INFO )
CHARACTER JOBQ, JOBU, JOBV
INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, NCYCLE, P
REAL TOLA, TOLB
REAL ALPHA( * ), BETA( * ), A( LDA, * ), B( LDB, * ), Q( LDQ,
* ), U( LDU, * ), V( LDV, * ), WORK( * )
PURPOSE
STGSJA computes the generalized singular value decomposition (GSVD) of two
real upper ``triangular (or trapezoidal)'' matrices A and B.
On entry, it is assumed that matrices A and B have the following forms,
which may be obtained by the preprocessing subroutine SGGSVP for two gen-
eral M-by-N matrix A and P-by-N matrix B:
If M-K-L >= 0
A = ( 0 A12 A13 ) K , B = ( 0 0 B13 ) L
( 0 0 A23 ) L ( 0 0 0 ) P-L
( 0 0 0 ) M-K-L N-K-L K L
N-K-L K L
if M-K-L < 0
A = ( 0 A12 A13 ) K , B = ( 0 0 B13 ) L
( 0 0 A23 ) M-K ( 0 0 0 ) P-L
N-K-L K L N-K-L K L
where K-by-K matrix A12 and L-by-L matrix B13 are nonsingular upper tri-
angular. A23 is L-by-L upper triangular if M-K-L > 0, otherwise A23 is L-
by-(M-K) upper trapezoidal.
On exit,
U'*A*Q = D1*( 0 R ), V'*B*Q = D2*( 0 R ),
where U, V and Q are orthogonal matrices, Z' denotes the transpose of Z, R
is a nonsingular upper triangular matrix, and D1 and D2 are ``diagonal''
matrices, which are of the following structures:
If M-K-L >= 0,
U'*A*Q = D1*( 0 R )
= K ( I 0 ) * ( 0 R11 R12 ) K
L ( 0 C ) ( 0 0 R22 ) L
M-K-L ( 0 0 ) N-K-L K L
K L
V'*B*Q = D2*( 0 R )
= L ( 0 S ) * ( 0 R11 R12 ) K
P-L ( 0 0 ) ( 0 0 R22 ) L
K L N-K-L K L
where
C = diag( ALPHA(K+1), ... , ALPHA(K+L) ),
S = diag( BETA(K+1), ... , BETA(K+L) ), C**2 + S**2 = I.
The nonsingular triangular matrix R = ( R11 R12 ) is stored
( 0 R22 )
in A(1:K+L,N-K-L+1:N) on exit.
If M-K-L < 0,
U'*A*Q = D1*( 0 R )
= K ( I 0 0 ) * ( 0 R11 R12 R13 ) K
M-K ( 0 C 0 ) ( 0 0 R22 R23 ) M-K
K M-K K+L-M ( 0 0 0 R33 ) K+L-M
N-K-L K M-K K+L-M
V'*B*Q = D2*( 0 R )
= M-K ( 0 S 0 ) * ( 0 R11 R12 R13 ) K
K+L-M ( 0 0 I ) ( 0 0 R22 R23 ) M-K
P-L ( 0 0 0 ) ( 0 0 0 R33 ) K+L-M
K M-K K+L-M N-K-L K M-K K+L-M
where
C = diag( ALPHA(K+1), ... , ALPHA(M) ),
S = diag( BETA(K+1), ... , BETA(M) ), C**2 + S**2 = I.
R = ( R11 R12 R13 ) is a nonsingular upper triangular matrix, the
( 0 R22 R23 )
( 0 0 R33 )
first M rows of R are stored in A(1:M, N-K-L+1:N) and R33 is stored in
B(M-K+1:L,N+M-K-L+1:N) on exit.
The computations of the orthogonal transformation matrices U, V and Q are
optional and may also be applied to the input orthogonal matrices U, V and
Q.
ARGUMENTS
JOBU (input) CHARACTER*1
= 'U': U is overwritten on the input orthogonal matrix U;
= 'I': U is initialized to the identity matrix;
= 'N': U is not computed.
JOBV (input) CHARACTER*1
= 'V': V is overwritten on the input orthogonal matrix V;
= 'I': V is initialized to the identity matrix;
= 'N': V is not computed.
JOBQ (input) CHARACTER*1
= 'Q': Q is overwritten on the input orthogonal matrix Q;
= 'I': Q is initialized to the identity matrix;
= 'N': Q is not computed.
M (input) INTEGER
The number of rows of the matrix A. M >= 0.
P (input) INTEGER
The number of rows of the matrix B. P >= 0.
N (input) INTEGER
The number of columns of the matrices A and B. N >= 0.
K (input) INTEGER
L (input) INTEGER K and L specify the subblocks in the input
matrices A and B:
A23 = A(K+1:MIN(K+L,M),N-L+1:N) and B13 = B(1:L,,N-L+1:N) of A and
B, whose GSVD is going to be computed by STGSJA. See Further
details.
A (input/output) REAL array, dimension (LDA,N)
On entry, the M-by-N matrix A. On exit, A(N-K+1:N,1:MIN(K+L,M) )
contains the triangular matrix R or part of R. See Purpose for
details.
LDA (input) INTEGER
The leading dimension of the array A. LDA >= max(1,M).
B (input/output) REAL array, dimension (LDB,N)
On entry, the P-by-N matrix B. On exit, if necessary, B(M-
K+1:L,N+M-K-L+1:N) contains a part of R. See Purpose for details.
LDB (input) INTEGER
The leading dimension of the array B. LDB >= max(1,P).
TOLA (input) REAL
TOLB (input) REAL TOLA and TOLB are the convergence criteria for
the Jacobi- Kogbetliantz iteration procedure. Generally, they are
the same as used in the preprocessing step, say TOLA =
MAX(M,N)*norm(A)*MACHEPS, TOLB = MAX(P,N)*norm(B)*MACHEPS.
ALPHA (output) REAL array, dimension (N)
BETA (output) REAL array, dimension (N) On exit, ALPHA and BETA
contain the generalized singular value pairs of A and B; If M-K-L
>= 0, ALPHA(1:K) = ONE, ALPHA(K+1:K+L) = diag(C),
BETA(1:K) = ZERO, BETA(K+1:K+L) = diag(S), and if M-K-L < 0,
ALPHA(1:K)= ONE, ALPHA(K+1:M)= C, ALPHA(M+1:K+L)= ZERO
BETA(1:K) = ZERO, BETA(K+1:M) = S, BETA(M+1:K+L) = ONE. Further-
more, if K+L < N, ALPHA(K+L+1:N) = ZERO
BETA(K+L+1:N) = ZERO.
U (input/output) REAL array, dimension (LDU,M)
On entry, if JOBU = 'U', U contains the orthogonal matrix U, On
exit, if JOBU = 'U', U is overwritten on the input orthogonal
matrix U. If JOBU = 'I', U is first set to the identity matrix. If
JOBU = 'N', U is not referenced.
LDU (input) INTEGER
The leading dimension of the array U. LDU >= max(1,M).
V (input/output) REAL array, dimension (LDV,P)
On entry, if JOBV = 'V', V contains the orthogonal matrix V. On
exit, if JOBV = 'V', V is overwritten on the input orthogonal
matrix V. If JOBV = 'I', U is first set to the identity matrix. If
JOBV = 'N', V is not referenced.
LDV (input) INTEGER
The leading dimension of the array V. LDV >= max(1,P).
Q (input/output) REAL array, dimension (LDQ,N)
On entry, if JOBQ = 'Q', Q contains the orthogonal matrix Q, On
exit, if JOBQ = 'Q', Q is overwritten on the input orthogonal
matrix Q. If JOBQ = 'I', Q is first set to the identity matrix. If
JOBQ = 'N', Q is not referenced.
LDQ (input) INTEGER
The leading dimension of the array Q. LDQ >= MAX(1,N).
WORK (workspace) REAL array, dimension (2*N)
NCYCLE (output) INTEGER
The number of cycles required for convergence.
INFO (output) INTEGER
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value.
= 1: the procedure does not converge after MAXIT cycles.
PARAMETERS
MAXIT INTEGER
MAXIT specifies the total loops that the iterative procedure may
take. If after MAXIT cycles, the routine fails to converge, we
return INFO = 1.
Further Details ===============
STGSJA essentially uses a variant of Kogbetliantz algorithm to
reduce min(L,M-K)-by-L triangular (or trapezoidal) matrix A23 and
L-by-L matrix B13 to the form:
U1'*A13*Q1 = C1*R1; V1'*B13*Q1 = S1*R1,
where U1, V1 and Q1 are orthogonal matrix, and Z' is the transpose
of Z. C1 and S1 are diagonal matrices satisfying
C1**2 + S1**2 = I,
and R1 is an L-by-L nonsingular upper triangular matrix.
Back to the listing of computational routines for orthogonal factorization and singular
value decomposition