.TH "SRC/stgsja.f" 3 "Version 3.12.0" "LAPACK" \" -*- nroff -*- .ad l .nh .SH NAME SRC/stgsja.f .SH SYNOPSIS .br .PP .SS "Functions/Subroutines" .in +1c .ti -1c .RI "subroutine \fBstgsja\fP (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)" .br .RI "\fBSTGSJA\fP " .in -1c .SH "Function/Subroutine Documentation" .PP .SS "subroutine stgsja (character jobu, character jobv, character jobq, integer m, integer p, integer n, integer k, integer l, real, dimension( lda, * ) a, integer lda, real, dimension( ldb, * ) b, integer ldb, real tola, real tolb, real, dimension( * ) alpha, real, dimension( * ) beta, real, dimension( ldu, * ) u, integer ldu, real, dimension( ldv, * ) v, integer ldv, real, dimension( ldq, * ) q, integer ldq, real, dimension( * ) work, integer ncycle, integer info)" .PP \fBSTGSJA\fP .PP \fBPurpose:\fP .RS 4 .PP .nf !> !> 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**T *A*Q = D1*( 0 R ), V**T *B*Q = D2*( 0 R ), !> !> where U, V and Q are orthogonal matrices\&. !> 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 !> ( 0 R ) = K ( 0 R11 R12 R13 ) !> 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\&. !> .fi .PP .RE .PP \fBParameters\fP .RS 4 \fIJOBU\fP .PP .nf !> JOBU is 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\&. !> .fi .PP .br \fIJOBV\fP .PP .nf !> JOBV is 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\&. !> .fi .PP .br \fIJOBQ\fP .PP .nf !> JOBQ is 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\&. !> .fi .PP .br \fIM\fP .PP .nf !> M is INTEGER !> The number of rows of the matrix A\&. M >= 0\&. !> .fi .PP .br \fIP\fP .PP .nf !> P is INTEGER !> The number of rows of the matrix B\&. P >= 0\&. !> .fi .PP .br \fIN\fP .PP .nf !> N is INTEGER !> The number of columns of the matrices A and B\&. N >= 0\&. !> .fi .PP .br \fIK\fP .PP .nf !> K is INTEGER !> .fi .PP .br \fIL\fP .PP .nf !> L is 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\&. !> .fi .PP .br \fIA\fP .PP .nf !> A is 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\&. !> .fi .PP .br \fILDA\fP .PP .nf !> LDA is INTEGER !> The leading dimension of the array A\&. LDA >= max(1,M)\&. !> .fi .PP .br \fIB\fP .PP .nf !> B is 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\&. !> .fi .PP .br \fILDB\fP .PP .nf !> LDB is INTEGER !> The leading dimension of the array B\&. LDB >= max(1,P)\&. !> .fi .PP .br \fITOLA\fP .PP .nf !> TOLA is REAL !> .fi .PP .br \fITOLB\fP .PP .nf !> TOLB is 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\&. !> .fi .PP .br \fIALPHA\fP .PP .nf !> ALPHA is REAL array, dimension (N) !> .fi .PP .br \fIBETA\fP .PP .nf !> BETA is 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\&. !> .fi .PP .br \fIU\fP .PP .nf !> U is 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\&. !> .fi .PP .br \fILDU\fP .PP .nf !> LDU is INTEGER !> The leading dimension of the array U\&. LDU >= max(1,M) if !> JOBU = 'U'; LDU >= 1 otherwise\&. !> .fi .PP .br \fIV\fP .PP .nf !> V is 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\&. !> .fi .PP .br \fILDV\fP .PP .nf !> LDV is INTEGER !> The leading dimension of the array V\&. LDV >= max(1,P) if !> JOBV = 'V'; LDV >= 1 otherwise\&. !> .fi .PP .br \fIQ\fP .PP .nf !> Q is 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\&. !> .fi .PP .br \fILDQ\fP .PP .nf !> LDQ is INTEGER !> The leading dimension of the array Q\&. LDQ >= max(1,N) if !> JOBQ = 'Q'; LDQ >= 1 otherwise\&. !> .fi .PP .br \fIWORK\fP .PP .nf !> WORK is REAL array, dimension (2*N) !> .fi .PP .br \fINCYCLE\fP .PP .nf !> NCYCLE is INTEGER !> The number of cycles required for convergence\&. !> .fi .PP .br \fIINFO\fP .PP .nf !> INFO is 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\&. !> .fi .PP .RE .PP .PP .nf !> Internal 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\&. !> .fi .PP .PP \fBAuthor\fP .RS 4 Univ\&. of Tennessee .PP Univ\&. of California Berkeley .PP Univ\&. of Colorado Denver .PP NAG Ltd\&. .RE .PP \fBFurther Details:\fP .RS 4 .PP .nf !> !> 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**T *A13*Q1 = C1*R1; V1**T *B13*Q1 = S1*R1, !> !> where U1, V1 and Q1 are orthogonal matrix, and Z**T 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\&. !> .fi .PP .RE .PP .PP Definition at line \fB375\fP of file \fBstgsja\&.f\fP\&. .SH "Author" .PP Generated automatically by Doxygen for LAPACK from the source code\&.