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Matrix data structures, linear solvers, least squares methods, eigenvalue,
and singular value decompositions. For larger random dense matrices (above ~ 350 x 350)
matrix-matrix multiplication C = A.B is about 50% faster than MTJ.
/*
* Copyright (C) 2003-2006 Bjørn-Ove Heimsund
*
* This file is part of MTJ.
*
* This library is free software; you can redistribute it and/or modify it
* under the terms of the GNU Lesser General Public License as published by the
* Free Software Foundation; either version 2.1 of the License, or (at your
* option) any later version.
*
* This library 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 Lesser General Public License
* for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with this library; if not, write to the Free Software Foundation,
* Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
package no.uib.cipr.matrix.sparse;
import no.uib.cipr.matrix.Matrix;
import no.uib.cipr.matrix.Vector;
/**
* Preconditioner interface. Before a preconditioner is used,
* setMatrix
must be called.
*/
public interface Preconditioner {
/**
* Solves the approximate problem with the given right hand side. Result is
* stored in given solution vector.
*
* @param b
* Right hand side of problem
* @param x
* Result is stored here
* @return x
*/
Vector apply(Vector b, Vector x);
/**
* Solves the approximate transpose problem with the given right hand side.
* Result is stored in given solution vector.
*
* @param b
* Right hand side of problem
* @param x
* Result is stored here
* @return x
*/
Vector transApply(Vector b, Vector x);
/**
* Sets the operator matrix for the preconditioner. This method must be
* called before a preconditioner is used by an iterative solver.
*
* @param A
* Matrix to setup the preconditioner for. Not modified
*/
void setMatrix(Matrix A);
}
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