*> \brief ZGGSVD computes the singular value decomposition (SVD) for OTHER matrices * * =========== DOCUMENTATION =========== * * Online html documentation available at * http://www.netlib.org/lapack/explore-html/ * *> \htmlonly *> Download ZGGSVD + dependencies *> *> [TGZ] *> *> [ZIP] *> *> [TXT] *> \endhtmlonly * * Definition: * =========== * * SUBROUTINE ZGGSVD( JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B, * LDB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK, * RWORK, IWORK, INFO ) * * .. Scalar Arguments .. * CHARACTER JOBQ, JOBU, JOBV * INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, P * .. * .. Array Arguments .. * INTEGER IWORK( * ) * DOUBLE PRECISION ALPHA( * ), BETA( * ), RWORK( * ) * COMPLEX*16 A( LDA, * ), B( LDB, * ), Q( LDQ, * ), * $ U( LDU, * ), V( LDV, * ), WORK( * ) * .. * * *> \par Purpose: * ============= *> *> \verbatim *> *> This routine is deprecated and has been replaced by routine ZGGSVD3. *> *> ZGGSVD computes the generalized singular value decomposition (GSVD) *> of an M-by-N complex matrix A and P-by-N complex matrix B: *> *> U**H*A*Q = D1*( 0 R ), V**H*B*Q = D2*( 0 R ) *> *> where U, V and Q are unitary matrices. *> Let K+L = the effective numerical rank of the *> matrix (A**H,B**H)**H, then R is a (K+L)-by-(K+L) nonsingular upper *> triangular matrix, D1 and D2 are M-by-(K+L) and P-by-(K+L) "diagonal" *> matrices and of the following structures, respectively: *> *> 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 ) *> L ( 0 0 R22 ) *> 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. *> *> (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 routine computes C, S, R, and optionally the unitary *> transformation matrices U, V and Q. *> *> In particular, if B is an N-by-N nonsingular matrix, then the GSVD of *> A and B implicitly gives the SVD of A*inv(B): *> A*inv(B) = U*(D1*inv(D2))*V**H. *> If ( A**H,B**H)**H has orthnormal columns, then the GSVD of A and B is also *> equal to the CS decomposition of A and B. Furthermore, the GSVD can *> be used to derive the solution of the eigenvalue problem: *> A**H*A x = lambda* B**H*B x. *> In some literature, the GSVD of A and B is presented in the form *> U**H*A*X = ( 0 D1 ), V**H*B*X = ( 0 D2 ) *> where U and V are orthogonal and X is nonsingular, and D1 and D2 are *> ``diagonal''. The former GSVD form can be converted to the latter *> form by taking the nonsingular matrix X as *> *> X = Q*( I 0 ) *> ( 0 inv(R) ) *> \endverbatim * * Arguments: * ========== * *> \param[in] JOBU *> \verbatim *> JOBU is CHARACTER*1 *> = 'U': Unitary matrix U is computed; *> = 'N': U is not computed. *> \endverbatim *> *> \param[in] JOBV *> \verbatim *> JOBV is CHARACTER*1 *> = 'V': Unitary matrix V is computed; *> = 'N': V is not computed. *> \endverbatim *> *> \param[in] JOBQ *> \verbatim *> JOBQ is CHARACTER*1 *> = 'Q': Unitary matrix Q is computed; *> = 'N': Q is not computed. *> \endverbatim *> *> \param[in] M *> \verbatim *> M is INTEGER *> The number of rows of the matrix A. M >= 0. *> \endverbatim *> *> \param[in] N *> \verbatim *> N is INTEGER *> The number of columns of the matrices A and B. N >= 0. *> \endverbatim *> *> \param[in] P *> \verbatim *> P is INTEGER *> The number of rows of the matrix B. P >= 0. *> \endverbatim *> *> \param[out] K *> \verbatim *> K is INTEGER *> \endverbatim *> *> \param[out] L *> \verbatim *> L is INTEGER *> *> On exit, K and L specify the dimension of the subblocks *> described in Purpose. *> K + L = effective numerical rank of (A**H,B**H)**H. *> \endverbatim *> *> \param[in,out] A *> \verbatim *> A is COMPLEX*16 array, dimension (LDA,N) *> On entry, the M-by-N matrix A. *> On exit, A contains the triangular matrix R, or part of R. *> See Purpose for details. *> \endverbatim *> *> \param[in] LDA *> \verbatim *> LDA is INTEGER *> The leading dimension of the array A. LDA >= max(1,M). *> \endverbatim *> *> \param[in,out] B *> \verbatim *> B is COMPLEX*16 array, dimension (LDB,N) *> On entry, the P-by-N matrix B. *> On exit, B contains part of the triangular matrix R if *> M-K-L < 0. See Purpose for details. *> \endverbatim *> *> \param[in] LDB *> \verbatim *> LDB is INTEGER *> The leading dimension of the array B. LDB >= max(1,P). *> \endverbatim *> *> \param[out] ALPHA *> \verbatim *> ALPHA is DOUBLE PRECISION array, dimension (N) *> \endverbatim *> *> \param[out] BETA *> \verbatim *> BETA is DOUBLE PRECISION 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) = C, *> BETA(K+1:K+L) = 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 *> and *> ALPHA(K+L+1:N) = 0 *> BETA(K+L+1:N) = 0 *> \endverbatim *> *> \param[out] U *> \verbatim *> U is COMPLEX*16 array, dimension (LDU,M) *> If JOBU = 'U', U contains the M-by-M unitary matrix U. *> If JOBU = 'N', U is not referenced. *> \endverbatim *> *> \param[in] LDU *> \verbatim *> LDU is INTEGER *> The leading dimension of the array U. LDU >= max(1,M) if *> JOBU = 'U'; LDU >= 1 otherwise. *> \endverbatim *> *> \param[out] V *> \verbatim *> V is COMPLEX*16 array, dimension (LDV,P) *> If JOBV = 'V', V contains the P-by-P unitary matrix V. *> If JOBV = 'N', V is not referenced. *> \endverbatim *> *> \param[in] LDV *> \verbatim *> LDV is INTEGER *> The leading dimension of the array V. LDV >= max(1,P) if *> JOBV = 'V'; LDV >= 1 otherwise. *> \endverbatim *> *> \param[out] Q *> \verbatim *> Q is COMPLEX*16 array, dimension (LDQ,N) *> If JOBQ = 'Q', Q contains the N-by-N unitary matrix Q. *> If JOBQ = 'N', Q is not referenced. *> \endverbatim *> *> \param[in] LDQ *> \verbatim *> LDQ is INTEGER *> The leading dimension of the array Q. LDQ >= max(1,N) if *> JOBQ = 'Q'; LDQ >= 1 otherwise. *> \endverbatim *> *> \param[out] WORK *> \verbatim *> WORK is COMPLEX*16 array, dimension (max(3*N,M,P)+N) *> \endverbatim *> *> \param[out] RWORK *> \verbatim *> RWORK is DOUBLE PRECISION array, dimension (2*N) *> \endverbatim *> *> \param[out] IWORK *> \verbatim *> IWORK is INTEGER array, dimension (N) *> On exit, IWORK stores the sorting information. More *> precisely, the following loop will sort ALPHA *> for I = K+1, min(M,K+L) *> swap ALPHA(I) and ALPHA(IWORK(I)) *> endfor *> such that ALPHA(1) >= ALPHA(2) >= ... >= ALPHA(N). *> \endverbatim *> *> \param[out] INFO *> \verbatim *> INFO is INTEGER *> = 0: successful exit. *> < 0: if INFO = -i, the i-th argument had an illegal value. *> > 0: if INFO = 1, the Jacobi-type procedure failed to *> converge. For further details, see subroutine ZTGSJA. *> \endverbatim * *> \par Internal Parameters: * ========================= *> *> \verbatim *> TOLA DOUBLE PRECISION *> TOLB DOUBLE PRECISION *> TOLA and TOLB are the thresholds to determine the effective *> rank of (A**H,B**H)**H. Generally, they are set to *> TOLA = MAX(M,N)*norm(A)*MAZHEPS, *> TOLB = MAX(P,N)*norm(B)*MAZHEPS. *> The size of TOLA and TOLB may affect the size of backward *> errors of the decomposition. *> \endverbatim * * Authors: * ======== * *> \author Univ. of Tennessee *> \author Univ. of California Berkeley *> \author Univ. of Colorado Denver *> \author NAG Ltd. * *> \date December 2016 * *> \ingroup complex16OTHERsing * *> \par Contributors: * ================== *> *> Ming Gu and Huan Ren, Computer Science Division, University of *> California at Berkeley, USA *> * ===================================================================== SUBROUTINE ZGGSVD( JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B, $ LDB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK, $ RWORK, IWORK, INFO ) * * -- LAPACK driver routine (version 3.7.0) -- * -- LAPACK is a software package provided by Univ. of Tennessee, -- * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..-- * December 2016 * * .. Scalar Arguments .. CHARACTER JOBQ, JOBU, JOBV INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, P * .. * .. Array Arguments .. INTEGER IWORK( * ) DOUBLE PRECISION ALPHA( * ), BETA( * ), RWORK( * ) COMPLEX*16 A( LDA, * ), B( LDB, * ), Q( LDQ, * ), $ U( LDU, * ), V( LDV, * ), WORK( * ) * .. * * ===================================================================== * * .. Local Scalars .. LOGICAL WANTQ, WANTU, WANTV INTEGER I, IBND, ISUB, J, NCYCLE DOUBLE PRECISION ANORM, BNORM, SMAX, TEMP, TOLA, TOLB, ULP, UNFL * .. * .. External Functions .. LOGICAL LSAME DOUBLE PRECISION DLAMCH, ZLANGE EXTERNAL LSAME, DLAMCH, ZLANGE * .. * .. External Subroutines .. EXTERNAL DCOPY, XERBLA, ZGGSVP, ZTGSJA * .. * .. Intrinsic Functions .. INTRINSIC MAX, MIN * .. * .. Executable Statements .. * * Decode and test the input parameters * WANTU = LSAME( JOBU, 'U' ) WANTV = LSAME( JOBV, 'V' ) WANTQ = LSAME( JOBQ, 'Q' ) * INFO = 0 IF( .NOT.( WANTU .OR. LSAME( JOBU, 'N' ) ) ) THEN INFO = -1 ELSE IF( .NOT.( WANTV .OR. LSAME( JOBV, 'N' ) ) ) THEN INFO = -2 ELSE IF( .NOT.( WANTQ .OR. LSAME( JOBQ, 'N' ) ) ) THEN INFO = -3 ELSE IF( M.LT.0 ) THEN INFO = -4 ELSE IF( N.LT.0 ) THEN INFO = -5 ELSE IF( P.LT.0 ) THEN INFO = -6 ELSE IF( LDA.LT.MAX( 1, M ) ) THEN INFO = -10 ELSE IF( LDB.LT.MAX( 1, P ) ) THEN INFO = -12 ELSE IF( LDU.LT.1 .OR. ( WANTU .AND. LDU.LT.M ) ) THEN INFO = -16 ELSE IF( LDV.LT.1 .OR. ( WANTV .AND. LDV.LT.P ) ) THEN INFO = -18 ELSE IF( LDQ.LT.1 .OR. ( WANTQ .AND. LDQ.LT.N ) ) THEN INFO = -20 END IF IF( INFO.NE.0 ) THEN CALL XERBLA( 'ZGGSVD', -INFO ) RETURN END IF * * Compute the Frobenius norm of matrices A and B * ANORM = ZLANGE( '1', M, N, A, LDA, RWORK ) BNORM = ZLANGE( '1', P, N, B, LDB, RWORK ) * * Get machine precision and set up threshold for determining * the effective numerical rank of the matrices A and B. * ULP = DLAMCH( 'Precision' ) UNFL = DLAMCH( 'Safe Minimum' ) TOLA = MAX( M, N )*MAX( ANORM, UNFL )*ULP TOLB = MAX( P, N )*MAX( BNORM, UNFL )*ULP * CALL ZGGSVP( JOBU, JOBV, JOBQ, M, P, N, A, LDA, B, LDB, TOLA, $ TOLB, K, L, U, LDU, V, LDV, Q, LDQ, IWORK, RWORK, $ WORK, WORK( N+1 ), INFO ) * * Compute the GSVD of two upper "triangular" matrices * CALL ZTGSJA( 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 ) * * Sort the singular values and store the pivot indices in IWORK * Copy ALPHA to RWORK, then sort ALPHA in RWORK * CALL DCOPY( N, ALPHA, 1, RWORK, 1 ) IBND = MIN( L, M-K ) DO 20 I = 1, IBND * * Scan for largest ALPHA(K+I) * ISUB = I SMAX = RWORK( K+I ) DO 10 J = I + 1, IBND TEMP = RWORK( K+J ) IF( TEMP.GT.SMAX ) THEN ISUB = J SMAX = TEMP END IF 10 CONTINUE IF( ISUB.NE.I ) THEN RWORK( K+ISUB ) = RWORK( K+I ) RWORK( K+I ) = SMAX IWORK( K+I ) = K + ISUB ELSE IWORK( K+I ) = K + I END IF 20 CONTINUE * RETURN * * End of ZGGSVD * END