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- *> \brief <b> CGGSVD3 computes the singular value decomposition (SVD) for OTHER matrices</b>
- *
- * =========== DOCUMENTATION ===========
- *
- * Online html documentation available at
- * http://www.netlib.org/lapack/explore-html/
- *
- *> \htmlonly
- *> Download CGGSVD3 + dependencies
- *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.tgz?format=tgz&filename=/lapack/lapack_routine/cggsvd3.f">
- *> [TGZ]</a>
- *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.zip?format=zip&filename=/lapack/lapack_routine/cggsvd3.f">
- *> [ZIP]</a>
- *> <a href="http://www.netlib.org/cgi-bin/netlibfiles.txt?format=txt&filename=/lapack/lapack_routine/cggsvd3.f">
- *> [TXT]</a>
- *> \endhtmlonly
- *
- * Definition:
- * ===========
- *
- * SUBROUTINE CGGSVD3( JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B,
- * LDB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ, WORK,
- * LWORK, RWORK, IWORK, INFO )
- *
- * .. Scalar Arguments ..
- * CHARACTER JOBQ, JOBU, JOBV
- * INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, P, LWORK
- * ..
- * .. Array Arguments ..
- * INTEGER IWORK( * )
- * REAL ALPHA( * ), BETA( * ), RWORK( * )
- * COMPLEX A( LDA, * ), B( LDB, * ), Q( LDQ, * ),
- * $ U( LDU, * ), V( LDV, * ), WORK( * )
- * ..
- *
- *
- *> \par Purpose:
- * =============
- *>
- *> \verbatim
- *>
- *> CGGSVD3 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 orthonormal 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 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 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 REAL array, dimension (N)
- *> \endverbatim
- *>
- *> \param[out] BETA
- *> \verbatim
- *> 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) = 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 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 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 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 array, dimension (MAX(1,LWORK))
- *> On exit, if INFO = 0, WORK(1) returns the optimal LWORK.
- *> \endverbatim
- *>
- *> \param[in] LWORK
- *> \verbatim
- *> LWORK is INTEGER
- *> The dimension of the array WORK.
- *>
- *> If LWORK = -1, then a workspace query is assumed; the routine
- *> only calculates the optimal size of the WORK array, returns
- *> this value as the first entry of the WORK array, and no error
- *> message related to LWORK is issued by XERBLA.
- *> \endverbatim
- *>
- *> \param[out] RWORK
- *> \verbatim
- *> RWORK is REAL 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 CTGSJA.
- *> \endverbatim
- *
- *> \par Internal Parameters:
- * =========================
- *>
- *> \verbatim
- *> TOLA REAL
- *> TOLB REAL
- *> 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)*MACHEPS,
- *> TOLB = MAX(P,N)*norm(B)*MACHEPS.
- *> 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.
- *
- *> \ingroup complexGEsing
- *
- *> \par Contributors:
- * ==================
- *>
- *> Ming Gu and Huan Ren, Computer Science Division, University of
- *> California at Berkeley, USA
- *>
- *
- *> \par Further Details:
- * =====================
- *>
- *> CGGSVD3 replaces the deprecated subroutine CGGSVD.
- *>
- * =====================================================================
- SUBROUTINE CGGSVD3( JOBU, JOBV, JOBQ, M, N, P, K, L, A, LDA, B,
- $ LDB, ALPHA, BETA, U, LDU, V, LDV, Q, LDQ,
- $ WORK, LWORK, RWORK, IWORK, INFO )
- *
- * -- LAPACK driver routine --
- * -- LAPACK is a software package provided by Univ. of Tennessee, --
- * -- Univ. of California Berkeley, Univ. of Colorado Denver and NAG Ltd..--
- *
- * .. Scalar Arguments ..
- CHARACTER JOBQ, JOBU, JOBV
- INTEGER INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M, N, P,
- $ LWORK
- * ..
- * .. Array Arguments ..
- INTEGER IWORK( * )
- REAL ALPHA( * ), BETA( * ), RWORK( * )
- COMPLEX A( LDA, * ), B( LDB, * ), Q( LDQ, * ),
- $ U( LDU, * ), V( LDV, * ), WORK( * )
- * ..
- *
- * =====================================================================
- *
- * .. Local Scalars ..
- LOGICAL WANTQ, WANTU, WANTV, LQUERY
- INTEGER I, IBND, ISUB, J, NCYCLE, LWKOPT
- REAL ANORM, BNORM, SMAX, TEMP, TOLA, TOLB, ULP, UNFL
- * ..
- * .. External Functions ..
- LOGICAL LSAME
- REAL CLANGE, SLAMCH
- EXTERNAL LSAME, CLANGE, SLAMCH
- * ..
- * .. External Subroutines ..
- EXTERNAL CGGSVP3, CTGSJA, SCOPY, XERBLA
- * ..
- * .. 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' )
- LQUERY = ( LWORK.EQ.-1 )
- LWKOPT = 1
- *
- * Test the input arguments
- *
- 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
- ELSE IF( LWORK.LT.1 .AND. .NOT.LQUERY ) THEN
- INFO = -24
- END IF
- *
- * Compute workspace
- *
- IF( INFO.EQ.0 ) THEN
- CALL CGGSVP3( JOBU, JOBV, JOBQ, M, P, N, A, LDA, B, LDB, TOLA,
- $ TOLB, K, L, U, LDU, V, LDV, Q, LDQ, IWORK, RWORK,
- $ WORK, WORK, -1, INFO )
- LWKOPT = N + INT( WORK( 1 ) )
- LWKOPT = MAX( 2*N, LWKOPT )
- LWKOPT = MAX( 1, LWKOPT )
- WORK( 1 ) = CMPLX( LWKOPT )
- END IF
- *
- IF( INFO.NE.0 ) THEN
- CALL XERBLA( 'CGGSVD3', -INFO )
- RETURN
- END IF
- IF( LQUERY ) THEN
- RETURN
- ENDIF
- *
- * Compute the Frobenius norm of matrices A and B
- *
- ANORM = CLANGE( '1', M, N, A, LDA, RWORK )
- BNORM = CLANGE( '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 = SLAMCH( 'Precision' )
- UNFL = SLAMCH( 'Safe Minimum' )
- TOLA = MAX( M, N )*MAX( ANORM, UNFL )*ULP
- TOLB = MAX( P, N )*MAX( BNORM, UNFL )*ULP
- *
- CALL CGGSVP3( 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 ), LWORK-N, INFO )
- *
- * Compute the GSVD of two upper "triangular" matrices
- *
- CALL CTGSJA( 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 SCOPY( 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
- *
- WORK( 1 ) = CMPLX( LWKOPT )
- RETURN
- *
- * End of CGGSVD3
- *
- END
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