ditsol_plscg 

Preconditioned least square conjugate gradient method (Serial and Parallel Versions)

FORMAT

  DITSOL_PLSCG (matvec, pcondl, pcondr, mstop, a, ia, x, b, n,
                ql, iql , qr, iqr, iparam, rparam, iwork, rwork, ierror)

Arguments

  DITSOL_PLSCG has the standard parameter list for an iterative solver.

Description

  The least squares conjugate gradient is a robust method for the  solution
  of general linear systems. It is equivalent to applying the conjugate
  gradient method to the normal equations:

       transp(A)* A * x = transp(A)* b

  This  method requires the evaluation of two matrix products, involving
  matrix A and transp(A). It suffers from the drawback that the condition
  number of transp(A) * A is the square of the condition number of A, and
  therefore the convergence of the method is slow. To alleviate the numerical
  instability  resulting from a straightforward application of the conjugate
  gradient method to the normal equations, CXML adopts the  implementation
  proposed in [Bjorck and Elfving 1979].

  The implementation of the least squares conjugate gradient  method requires
  the routine MATVEC to provide operations for  both job= 0 and job= 1. The
  routines MATVEC, PCONDL (if used), PCONDR (if used) and MSTOP (if used)
  should be declared external in your calling (sub)program.

  CXML provides the following four forms of the method:

  •  Unpreconditioned least squares conjugate gradient method:

     This is the conjugate gradient method applied to

         transp(A) * A * x = transp(A) * b

     where A is a general matrix. As no preconditioning is used,  both PCONDL
     and PCONDR are dummy input parameters.

     For the unpreconditioned least squares conjugate gradient  method, the
     length of the real work space array, defined by the variable nrwk
     (IPARAM(4)), should be at least 4*n, where  n is the order of the matrix
     A.

     The vector z, passed as an input argument to the routine  MSTOP, is not
     defined.

  •  Least squares conjugate gradient method with left preconditioning:

     This is the conjugate gradient method applied to

        ( transp(A) * inverse(transp(QL)) *  inverse(QL)  * A) * x  =
            (transp(A) * inverse(transp(QL)) * inverse(QL) * b )

     The routine PCONDL, with job= 0 should evaluate

        v = inverse(QL) * u

     and with job= 1 should evaluate

        v = inv_transp(QL) * u

     The routine PCONDR is not used and is therefore a dummy input parameter.

     For the least squares conjugate gradient method, with left
     preconditioning, the length of the real work space array,  defined by
     the variable nrwk (IPARAM(4)), should be at  least 5*n, where n is the
     order of the matrix A. This does  not include the memory requirements of
     the preconditioner.

     The vector z, passed as an input argument to the routine  MSTOP, is
     defined as

         z = inverse(QL) * r

     where r is the residual at the i-th iteration.

  •  Least squares conjugate gradient method with right preconditioning:

     This is the conjugate gradient method applied to

        ( inverse(transp(QR)) * transp(A) * A * inverse(QR) ) * y  =
              ( inverse(transp(QR)) * transp(A) * b )

     where

        y = QR * x

     The routine PCONDR, with job= 0 should evaluate

        v = inverse(QR) * u

     and with job= 1 should evaluate

        v = inv_transp(QR) * u

     The routine PCONDL is not used and is therefore a dummy input parameter.

     For the least squares conjugate gradient method, with right
     preconditioning, the length of the real work space array,  defined by
     the variable nrwk (IPARAM(4)), should be at least  5*n, where n is the
     order of the matrix A. This does not  include the memory requirements of
     the preconditioner.

     The vector z, passed as an input argument to the routine  MSTOP, is not
     defined.

  •  Least squares conjugate gradient method with split preconditioning:

     This is the conjugate gradient method applied to

        ( inverse(transp(QR)) * transp(A) * inverse(transp(QL) *
            inverse(QL)  * A * inverse(QR)) * y  =
            (inverse(transp(QR)) * transp(A) * inverse(transp(QL)) *
            inverse(QL) * b)

     where

        y = QR * x

     The routine PCONDL, with job= 0 should evaluate

        v = inverse(QL) * u

     and with job= 1 should evaluate

        v = inv_transp(QL)* u

     The routine PCONDR, with job= 0 should evaluate

        v = inverse(QR) * u

     and with job= 1 should evaluate

        v = inv_transp(QR) * u

     For the least squares conjugate gradient method, with split
     preconditioning, the length of the real work space array, defined by the
     variable nrwk (IPARAM(4)), should be at least  6*n, where n is the order
     of the matrix A. This does not  include the memory requirements of the
     preconditioner.

     The vector z, passed as an input argument to the routine MSTOP, is
     defined as

        z = inverse(QL) * r

     where r is the residual at the i-th iteration.

  This routine is available in both serial and parallel versions. The routine
  names and parameter list are identical for both versions. For information
  about linking to the serial or to the parallel library, refer to the CXML
  Reference Manual.