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PDGESVD(l)			       )			    PDGESVD(l)

NAME
       PDGESVD	-  compute the singular value decomposition (SVD) of an M-by-N
       matrix A, optionally computing the left and/or right singular vectors

SYNOPSIS
       SUBROUTINE PDGESVD( JOBU, JOBVT, M, N, A, IA, JA, DESCA, S, U, IU,  JU,
			   DESCU, VT, IVT, JVT, DESCVT, WORK, LWORK, INFO )

	   CHARACTER	   JOBU, JOBVT

	   INTEGER	   IA, INFO, IU, IVT, JA, JU, JVT, LWORK, M, N

	   INTEGER	   DESCA( * ), DESCU( * ), DESCVT( * )

	   DOUBLE	   PRECISION  A( * ), S( * ), U( * ), VT( * ), WORK( *
			   )

PURPOSE
       PDGESVD computes the singular value decomposition (SVD)	of  an	M-by-N
       matrix  A, optionally computing the left and/or right singular vectors.
       The SVD is written as
	    A = U * SIGMA * transpose(V)

       where SIGMA is an M-by-N matrix which is zero except for	 its  min(M,N)
       diagonal elements, U is an M-by-M orthogonal matrix, and V is an N-by-N
       orthogonal matrix. The diagonal elements of SIGMA are the singular val‐
       ues  of	A  and	the columns of U and V are the corresponding right and
       left singular vectors, respectively. The singular values	 are  returned
       in array S in decreasing order and only the first min(M,N) columns of U
       and rows of VT = V**T are computed.

       Notes
       =====
       Each global data object is described by an associated description  vec‐
       tor.  This vector stores the information required to establish the map‐
       ping between an object element and its corresponding process and memory
       location.

       Let  A  be  a generic term for any 2D block cyclicly distributed array.
       Such a global array has an associated description vector DESCA.	In the
       following  comments,  the  character _ should be read as "of the global
       array".

       NOTATION	       STORED IN      EXPLANATION
       ---------------	--------------	--------------------------------------
       DTYPE_A(global) DESCA( DTYPE_ )The descriptor type.  In this case,
				      DTYPE_A = 1.
       CTXT_A (global) DESCA( CTXT_ ) The BLACS context handle, indicating
				      the BLACS process grid A is distribu-
				      ted over. The context itself is glo-
				      bal, but the handle (the integer
				      value) may vary.
       M_A    (global) DESCA( M_ )    The number of rows in the global
				      array A.
       N_A    (global) DESCA( N_ )    The number of columns in the global
				      array A.
       MB_A   (global) DESCA( MB_ )   The blocking factor used to distribute
				      the rows of the array.
       NB_A   (global) DESCA( NB_ )   The blocking factor used to distribute
				      the columns of the array.
       RSRC_A (global) DESCA( RSRC_ ) The process row over which the first
				      row  of  the  array  A  is  distributed.
       CSRC_A (global) DESCA( CSRC_ ) The process column over which the
				      first column of the array A is
				      distributed.
       LLD_A  (local)  DESCA( LLD_ )  The leading dimension of the local
				      array.  LLD_A >= MAX(1,LOCr(M_A)).

       Let K be the number of rows or columns of  a  distributed  matrix,  and
       assume that its process grid has dimension r x c. LOCr( K ) denotes the
       number of elements of K that a process would receive if K were distrib‐
       uted  over  the r processes of its process column. Similarly, LOCc( K )
       denotes the number of elements of K that a process would receive	 if  K
       were distributed over the c processes of its process row. The values of
       LOCr() and LOCc() may be determined via a call to  the  ScaLAPACK  tool
       function, NUMROC:
	       LOCr( M ) = NUMROC( M, MB_A, MYROW, RSRC_A, NPROW ),
	       LOCc(  N ) = NUMROC( N, NB_A, MYCOL, CSRC_A, NPCOL ).  An upper
       bound for these quantities may be computed by:
	       LOCr( M ) <= ceil( ceil(M/MB_A)/NPROW )*MB_A
	       LOCc( N ) <= ceil( ceil(N/NB_A)/NPCOL )*NB_A

ARGUMENTS
       MP = number of local rows in A and U NQ = number of local columns in  A
       and  VT SIZE = min( M, N ) SIZEQ = number of local columns in U SIZEP =
       number of local rows in VT

       JOBU    (global input) CHARACTER*1
	       Specifies options for computing U:
	       = 'V':  the first SIZE columns of U (the left singular vectors)
	       are  returned  in the array U; = 'N':  no columns of U (no left
	       singular vectors) are computed.

       JOBVT   (global input) CHARACTER*1
	       Specifies options for computing V**T:
	       = 'V':  the first SIZE rows of V**T (the	 right	singular  vec‐
	       tors) are returned in the array VT; = 'N':  no rows of V**T (no
	       right singular vectors) are computed.

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

       N       (global input) INTEGER
	       The number of columns of the input matrix A.  N >= 0.

       A       (local input/workspace) block cyclic DOUBLE PRECISION array,
	       global dimension (M, N), local dimension (MP, NQ) On exit,  the
	       contents of A are destroyed.

       IA      (global input) INTEGER
	       The row index in the global array A indicating the first row of
	       sub( A ).

       JA      (global input) INTEGER
	       The column index in the global array  A	indicating  the	 first
	       column of sub( A ).

       DESCA   (global input) INTEGER array of dimension DLEN_
	       The array descriptor for the distributed matrix A.

       S       (global output) DOUBLE PRECISION array, dimension SIZE
	       The singular values of A, sorted so that S(i) >= S(i+1).

       U       (local output) DOUBLE PRECISION array, local dimension
	       (MP,  SIZEQ),  global dimension (M, SIZE) if JOBU = 'V', U con‐
	       tains the first min(m,n) columns of U if JOBU = 'N', U  is  not
	       referenced.

       IU      (global input) INTEGER
	       The row index in the global array U indicating the first row of
	       sub( U ).

       JU      (global input) INTEGER
	       The column index in the global array  U	indicating  the	 first
	       column of sub( U ).

       DESCU   (global input) INTEGER array of dimension DLEN_
	       The array descriptor for the distributed matrix U.

       VT      (local output) DOUBLE PRECISION array, local dimension
	       (SIZEP,	NQ),  global  dimension (SIZE, N).  If JOBVT = 'V', VT
	       contains the first SIZE rows of V**T. If JOBVT = 'N', VT is not
	       referenced.

       IVT     (global input) INTEGER
	       The  row	 index in the global array VT indicating the first row
	       of sub( VT ).

       JVT     (global input) INTEGER
	       The column index in the global array VT	indicating  the	 first
	       column of sub( VT ).

       DESCVT	(global input) INTEGER array of dimension DLEN_
		The array descriptor for the distributed matrix VT.

       WORK    (local workspace/output) DOUBLE PRECISION array, dimension
	       (LWORK)	On  exit,  if  INFO  =	0, WORK(1) returns the optimal
	       LWORK.

       LWORK   (local input) INTEGER
	       The dimension of the array WORK.

	       LWORK > 2 + 6*SIZEB + MAX(WATOBD, WBDTOSVD),

	       where SIZEB = MAX(M,N), and WATOBD and WBDTOSVD refer,  respec‐
	       tively, to the workspace required to bidiagonalize the matrix A
	       and to go from the bidiagonal  matrix  to  the  singular	 value
	       decomposition U*S*VT.

	       For WATOBD, the following holds:

	       WATOBD	 =   MAX(MAX(WPDLANGE,WPDGEBRD),   MAX(WPDLARED2D,WPD‐
	       LARED1D)),

	       where  WPDLANGE,	 WPDLARED1D,  WPDLARED2D,  WPDGEBRD  are   the
	       workspaces  required  respectively for the subprograms PDLANGE,
	       PDLARED1D, PDLARED2D, PDGEBRD. Using the standard notation

	       MP = NUMROC( M, MB, MYROW, DESCA( CTXT_ ), NPROW), NQ = NUMROC(
	       N, NB, MYCOL, DESCA( LLD_ ), NPCOL),

	       the workspaces required for the above subprograms are

	       WPDLANGE	 =  MP, WPDLARED1D = NQ0, WPDLARED2D = MP0, WPDGEBRD =
	       NB*(MP + NQ + 1) + NQ,

	       where NQ0 and MP0 refer, respectively, to the  values  obtained
	       at MYCOL = 0 and MYROW = 0. In general, the upper limit for the
	       workspace is given by a workspace required on processor (0,0):

	       WATOBD <= NB*(MP0 + NQ0 + 1) + NQ0.

	       In case of a homogeneous process grid this upper limit  can  be
	       used  as an estimate of the minimum workspace for every proces‐
	       sor.

	       For WBDTOSVD, the following holds:

	       WBDTOSVD	 =  SIZE*(WANTU*NRU  +	WANTVT*NCVT)  +	  MAX(WDBDSQR,
	       MAX(WANTU*WPDORMBRQLN, WANTVT*WPDORMBRPRT)),

	       where

	       1,  if  left(right) singular vectors are wanted WANTU(WANTVT) =
	       0, otherwise

	       and WDBDSQR, WPDORMBRQLN and WPDORMBRPRT refer respectively  to
	       the    workspace	  required   for   the	 subprograms   DBDSQR,
	       PDORMBR(QLN), and PDORMBR(PRT), where QLN and PRT are the  val‐
	       ues  of	the  arguments	VECT,  SIDE,  and TRANS in the call to
	       PDORMBR. NRU is equal to the local number of rows of the matrix
	       U  when distributed 1-dimensional "column" of processes. Analo‐
	       gously, NCVT is equal to the local number  of  columns  of  the
	       matrix  VT  when distributed across 1-dimensional "row" of pro‐
	       cesses. Calling the LAPACK procedure DBDSQR requires

	       WDBDSQR = MAX(1, 2*SIZE + (2*SIZE - 4)*MAX(WANTU, WANTVT))

	       on every processor. Finally,

	       WPDORMBRQLN = MAX( (NB*(NB-1))/2, (SIZEQ+MP)*NB)+NB*NB, WPDORM‐
	       BRPRT = MAX( (MB*(MB-1))/2, (SIZEP+NQ)*MB )+MB*MB,

	       If LWORK = -1, then LWORK is global input and a workspace query
	       is assumed; the routine only calculates the  minimum  size  for
	       the work array. The required workspace is returned as the first
	       element of WORK and no error message is issued by PXERBLA.

       INFO    (global output) INTEGER
	       = 0:  successful exit.
	       < 0:  if INFO = -i, the i-th argument had an illegal value.
	       > 0:  if SBDSQR did not converge If INFO = MIN(M,N) +  1,  then
	       PDGESVD	has detected heterogeneity by finding that eigenvalues
	       were not identical across the process grid. In this  case,  the
	       accuracy of the results from PDGESVD cannot be guaranteed.

ScaLAPACK version 1.7		13 August 2001			    PDGESVD(l)
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