ZGGSVD - compute the generalized singular value decomposi­
       tion (GSVD) of an M-by-N complex matrix A and P-by-N  com­
       plex matrix B


SYNOPSIS

       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 )

           CHARACTER      JOBQ, JOBU, JOBV

           INTEGER        INFO, K, L, LDA, LDB, LDQ, LDU, LDV, M,
                          N, P

           INTEGER        IWORK( * )

           DOUBLE         PRECISION ALPHA( * ), BETA( * ), RWORK(
                          * )

           COMPLEX*16     A( LDA, * ), B( LDB, * ), Q( LDQ, *  ),
                          U( LDU, * ), V( LDV, * ), WORK( * )


PURPOSE

       ZGGSVD  computes the generalized singular value decomposi­
       tion (GSVD) of an M-by-N complex matrix A and P-by-N  com­
       plex matrix B:
             U'*A*Q = D1*( 0 R ),    V'*B*Q = D2*( 0 R )

       where  U,  V  and Q are unitary matrices, and Z' means the
       conjugate transpose of Z.  Let K+L = the effective numeri­
       cal   rank   of   the   matrix   (A',B')',  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) ),


         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'.
       If ( A',B')' 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'*A x = lambda* B'*B x.
       In  some  literature,  the GSVD of A and B is presented in
       the form
                        U'*A*X = ( 0 D1 ),   V'*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 con­
       verted to the latter form by taking the nonsingular matrix
       X as

                             X = Q*(  I   0    )
                                   (  0 inv(R) )

       JOBU    (input) CHARACTER*1
               = 'U':  Unitary matrix U is computed;
               = 'N':  U is not computed.

       JOBV    (input) CHARACTER*1
               = 'V':  Unitary matrix V is computed;
               = 'N':  V is not computed.

       JOBQ    (input) CHARACTER*1
               = 'Q':  Unitary matrix Q is computed;
               = 'N':  Q is not computed.

       M       (input) INTEGER
               The number of rows of the matrix A.  M >= 0.

       N       (input) INTEGER
               The  number of columns of the matrices A and B.  N
               >= 0.

       P       (input) INTEGER
               The number of rows of the matrix B.  P >= 0.

       K       (output) INTEGER
               L       (output) INTEGER On exit, K and L  specify
               the  dimension  of the subblocks described in Pur­
               pose.   K  +  L  =  effective  numerical  rank  of
               (A',B')'.

       A       (input/output) COMPLEX*16 array, dimension (LDA,N)
               On entry, the M-by-N matrix A.  On  exit,  A  con­
               tains  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) COMPLEX*16 array, dimension (LDB,N)
               On entry, the P-by-N matrix B.  On  exit,  B  con­
               tains  part  of the triangular matrix R if M-K-L <
               0.  See Purpose for details.

       LDB     (input) INTEGER
               The leading dimension  of  the  array  B.  LDB  >=
               max(1,P).

       ALPHA   (output) DOUBLE PRECISION array, dimension (N)
               BETA    (output) DOUBLE PRECISION array, dimension
               (N) On exit, ALPHA and BETA contain  the  general­
               ized 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,

               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

       U       (output) 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.

       LDU     (input) INTEGER
               The leading dimension  of  the  array  U.  LDU  >=
               max(1,M) if JOBU = 'U'; LDU >= 1 otherwise.

       V       (output) 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.

       LDV     (input) INTEGER
               The leading dimension  of  the  array  V.  LDV  >=
               max(1,P) if JOBV = 'V'; LDV >= 1 otherwise.

       Q       (output) 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.

       LDQ     (input) INTEGER
               The leading dimension  of  the  array  Q.  LDQ  >=
               max(1,N) if JOBQ = 'Q'; LDQ >= 1 otherwise.

       WORK    (workspace) COMPLEX*16 array, dimension
               (max(3*N,M,P)+N)

       RWORK   (workspace) DOUBLE PRECISION array, dimension
               (2*N)

       IWORK   (workspace/output) 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).

       INFO    (output)INTEGER
               = 0:  successful exit.
               < 0:  if INFO = -i, the i-th argument had an ille­
               gal value.
               > 0:  if  INFO  =  1,  the  Jacobi-type  procedure
               failed to converge.  For further details, see sub­
               routine ZTGSJA.


PARAMETERS

       TOLA    DOUBLE PRECISION
               TOLB    DOUBLE PRECISION TOLA  and  TOLB  are  the

               (A',B')'.  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.

               Further Details ===============

               2-96 Based on modifications by Ming  Gu  and  Huan
               Ren, Computer Science Division, University of Cal­
               ifornia at Berkeley, USA


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