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A fast and easy to use dense matrix linear algebra library written in Java.
There is a newer version: 0.25
/*
 * Copyright (c) 2009-2012, Peter Abeles. All Rights Reserved.
 *
 * This file is part of Efficient Java Matrix Library (EJML).
 *
 * EJML 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 3
 * of the License, or (at your option) any later version.
 *
 * EJML 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 EJML.  If not, see .
 */

package org.ejml.alg.dense.linsol.lu;

import org.ejml.alg.dense.decomposition.lu.LUDecompositionBase;
import org.ejml.alg.dense.linsol.LinearSolverAbstract;
import org.ejml.data.DenseMatrix64F;


/**
 * @author Peter Abeles
 */
public abstract class LinearSolverLuBase extends LinearSolverAbstract {

    protected LUDecompositionBase decomp;

    public LinearSolverLuBase( LUDecompositionBase decomp ) {
        this.decomp = decomp;

    }

    @Override
    public boolean setA(DenseMatrix64F A) {
        _setA(A);

        return decomp.decompose(A);
    }

    @Override
    public double quality() {
        return decomp.quality();
    }

    @Override
    public void invert(DenseMatrix64F A_inv) {
        double []vv = decomp._getVV();
        DenseMatrix64F LU = decomp.getLU();
        
        if( A_inv.numCols != LU.numCols || A_inv.numRows != LU.numRows )
            throw new IllegalArgumentException("Unexpected matrix dimension");

        int n = A.numCols;

        double dataInv[] = A_inv.data;

        for( int j = 0; j < n; j++ ) {
            // don't need to change inv into an identity matrix before hand
            for( int i = 0; i < n; i++ ) vv[i] = i == j ? 1 : 0;
            decomp._solveVectorInternal(vv);
//            for( int i = 0; i < n; i++ ) dataInv[i* n +j] = vv[i];
            int index = j;
            for( int i = 0; i < n; i++ , index += n) dataInv[ index ] = vv[i];
        }
    }

    /**
     * This attempts to improve upon the solution generated by account
     * for numerical imprecisions.  See numerical recipes for more information.  It
     * is assumed that solve has already been run on 'b' and 'x' at least once.
     *
     * @param b A matrix. Not modified.
     * @param x A matrix. Modified.
     */
    public void improveSol( DenseMatrix64F b , DenseMatrix64F x )
    {
        if( b.numCols != x.numCols ) {
            throw new IllegalArgumentException("bad shapes");
        }

        double dataA[] = A.data;
        double dataB[] = b.data;
        double dataX[] = x.data;

        final int nc = b.numCols;
        final int n = b.numCols;

        double []vv = decomp._getVV();
        DenseMatrix64F LU = decomp.getLU();

//        BigDecimal sdp = new BigDecimal(0);
        for( int k = 0; k < nc; k++ ) {
            for( int i = 0; i < n; i++ ) {
                // *NOTE* in the book this is a long double.  extra precision might be required
                double sdp = -dataB[ i * nc + k];
//                BigDecimal sdp = new BigDecimal(-dataB[ i * nc + k]);
                for( int j = 0; j < n; j++ ) {
                    sdp += dataA[i* n +j] * dataX[ j * nc + k];
//                    sdp = sdp.add( BigDecimal.valueOf(dataA[i* n +j] * dataX[ j * nc + k]));
                }
                vv[i] = sdp;
//                vv[i] = sdp.doubleValue();
            }
            decomp._solveVectorInternal(vv);
            for( int i = 0; i < n; i++ ) {
                dataX[i*nc + k] -= vv[i];
            }
        }
    }

    @Override
    public boolean modifiesA() {
        return false;
    }

    @Override
    public boolean modifiesB() {
        return false;
    }
}