CXML
STGSJA (3lapack)
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 from a general
M-by-N matrix A and P-by-N matrix B:
N-K-L K L
A = K ( 0 A12 A13 ) if M-K-L >= 0;
L ( 0 0 A23 )
M-K-L ( 0 0 0 )
N-K-L K L
A = K ( 0 A12 A13 ) if M-K-L < 0;
M-K ( 0 0 A23 )
N-K-L K L
B = L ( 0 0 B13 )
P-L ( 0 0 0 )
where the K-by-K matrix A12 and L-by-L matrix B13 are nonsingular upper
triangular; A23 is L-by-L upper triangular if M-K-L >= 0, otherwise A23 is
(M-K)-by-L 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,
K L
D1 = K ( I 0 )
L ( 0 C )
M-K-L ( 0 0 )
K L
D2 = L ( 0 S )
P-L ( 0 0 )
N-K-L K L
( 0 R ) = K ( 0 R11 R12 ) K
L ( 0 0 R22 ) L
where
C = diag( ALPHA(K+1), ... , ALPHA(K+L) ),
S = diag( BETA(K+1), ... , BETA(K+L) ),
C**2 + S**2 = I.
R is stored in A(1:K+L,N-K-L+1:N) on exit.
If M-K-L < 0,
K M-K K+L-M
D1 = K ( I 0 0 )
M-K ( 0 C 0 )
K M-K K+L-M
D2 = M-K ( 0 S 0 )
K+L-M ( 0 0 I )
P-L ( 0 0 0 )
N-K-L K M-K K+L-M
M-K ( 0 0 R22 R23 )
K+L-M ( 0 0 0 R33 )
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 stored in A(1:M, N-K-L+1:N) and R33 is stored
( 0 R22 R23 )
in B(M-K+1:L,N+M-K-L+1:N) on exit.
The computation of the orthogonal transformation matrices U, V or Q is
optional. These matrices may either be formed explicitly, or they may be
postmultiplied into input matrices U1, V1, or Q1.
ARGUMENTS
JOBU (input) CHARACTER*1
= 'U': U must contain an orthogonal matrix U1 on entry, and the
product U1*U is returned; = 'I': U is initialized to the unit
matrix, and the orthogonal matrix U is returned; = 'N': U is not
computed.
JOBV (input) CHARACTER*1
= 'V': V must contain an orthogonal matrix V1 on entry, and the
product V1*V is returned; = 'I': V is initialized to the unit
matrix, and the orthogonal matrix V is returned; = 'N': V is not
computed.
JOBQ (input) CHARACTER*1
= 'Q': Q must contain an orthogonal matrix Q1 on entry, and the
product Q1*Q is returned; = 'I': Q is initialized to the unit
matrix, and the orthogonal matrix Q is returned; = '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; ALPHA(1:K)
= 1,
BETA(1:K) = 0, and if M-K-L >= 0, ALPHA(K+1:K+L) = diag(C),
BETA(K+1:K+L) = diag(S), or if M-K-L < 0, ALPHA(K+1:M)= C,
ALPHA(M+1:K+L)= 0
BETA(K+1:M) = S, BETA(M+1:K+L) = 1. Furthermore, if K+L < N,
ALPHA(K+L+1:N) = 0 and
BETA(K+L+1:N) = 0.
U (input/output) REAL array, dimension (LDU,M)
On entry, if JOBU = 'U', U must contain a matrix U1 (usually the
orthogonal matrix returned by SGGSVP). On exit, if JOBU = 'I', U
contains the orthogonal matrix U; if JOBU = 'U', U contains the
product U1*U. If JOBU = 'N', U is not referenced.
LDU (input) INTEGER
The leading dimension of the array U. LDU >= max(1,M) if JOBU =
'U'; LDU >= 1 otherwise.
V (input/output) REAL array, dimension (LDV,P)
On entry, if JOBV = 'V', V must contain a matrix V1 (usually the
orthogonal matrix returned by SGGSVP). On exit, if JOBV = 'I', V
contains the orthogonal matrix V; if JOBV = 'V', V contains the
product V1*V. If JOBV = 'N', V is not referenced.
LDV (input) INTEGER
The leading dimension of the array V. LDV >= max(1,P) if JOBV =
'V'; LDV >= 1 otherwise.
Q (input/output) REAL array, dimension (LDQ,N)
On entry, if JOBQ = 'Q', Q must contain a matrix Q1 (usually the
orthogonal matrix returned by SGGSVP). On exit, if JOBQ = 'I', Q
contains the orthogonal matrix Q; if JOBQ = 'Q', Q contains the
product Q1*Q. If JOBQ = 'N', Q is not referenced.
LDQ (input) INTEGER
The leading dimension of the array Q. LDQ >= max(1,N) if JOBQ =
'Q'; LDQ >= 1 otherwise.
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.
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