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SUBROUTINE slSVDS (M, N, MP, NP, B, U, W, V, WORK, X)
*+
* - - - - - - -
* S V D S
* - - - - - - -
*
* From a given vector and the SVD of a matrix (as obtained from
* the SVD routine), obtain the solution vector (double precision)
*
* This routine solves the equation:
*
* A . x = b
*
* where:
*
* A is a given M (rows) x N (columns) matrix, where M.GE.N
* x is the N-vector we wish to find
* b is a given M-vector
*
* by means of the Singular Value Decomposition method (SVD). In
* this method, the matrix A is first factorised (for example by
* the routine slSVD) into the following components:
*
* A = U x W x VT
*
* where:
*
* A is the M (rows) x N (columns) matrix
* U is an M x N column-orthogonal matrix
* W is an N x N diagonal matrix with W(I,I).GE.0
* VT is the transpose of an NxN orthogonal matrix
*
* Note that M and N, above, are the LOGICAL dimensions of the
* matrices and vectors concerned, which can be located in
* arrays of larger PHYSICAL dimensions MP and NP.
*
* The solution is found from the expression:
*
* x = V . [diag(1/Wj)] . (transpose(U) . b)
*
* Notes:
*
* 1) If matrix A is square, and if the diagonal matrix W is not
* adjusted, the method is equivalent to conventional solution
* of simultaneous equations.
*
* 2) If M>N, the result is a least-squares fit.
*
* 3) If the solution is poorly determined, this shows up in the
* SVD factorisation as very small or zero Wj values. Where
* a Wj value is small but non-zero it can be set to zero to
* avoid ill effects. The present routine detects such zero
* Wj values and produces a sensible solution, with highly
* correlated terms kept under control rather than being allowed
* to elope to infinity, and with meaningful values for the
* other terms.
*
* Given:
* M,N i numbers of rows and columns in matrix A
* MP,NP i physical dimensions of array containing matrix A
* B d(M) known vector b
* U d(MP,NP) array containing MxN matrix U
* W d(N) NxN diagonal matrix W (diagonal elements only)
* V d(NP,NP) array containing NxN orthogonal matrix V
*
* Returned:
* WORK d(N) workspace
* X d(N) unknown vector x
*
* Reference:
* Numerical Recipes, section 2.9.
*
* P.T.Wallace Starlink 29 October 1993
*
* Copyright (C) 1995 Rutherford Appleton Laboratory
*
* License:
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program (see SLA_CONDITIONS); if not, write to the
* Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor,
* Boston, MA 02110-1301 USA
*
* Copyright (C) 1995 Association of Universities for Research in Astronomy Inc.
*-
IMPLICIT NONE
INTEGER M,N,MP,NP
DOUBLE PRECISION B(M),U(MP,NP),W(N),V(NP,NP),WORK(N),X(N)
INTEGER J,I,JJ
DOUBLE PRECISION S
* Calculate [diag(1/Wj)] . transpose(U) . b (or zero for zero Wj)
DO J=1,N
S=0D0
IF (W(J).NE.0D0) THEN
DO I=1,M
S=S+U(I,J)*B(I)
END DO
S=S/W(J)
END IF
WORK(J)=S
END DO
* Multiply by matrix V to get result
DO J=1,N
S=0D0
DO JJ=1,N
S=S+V(J,JJ)*WORK(JJ)
END DO
X(J)=S
END DO
END
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