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A fast and easy to use dense matrix linear algebra library written in Java.

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/*
 * Copyright (c) 2009-2011, 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.chol;

import org.ejml.alg.dense.decomposition.TriangularSolver;
import org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL;
import org.ejml.alg.dense.linsol.LinearSolverAbstract;
import org.ejml.data.DenseMatrix64F;
import org.ejml.ops.SpecializedOps;


/**
 * @author Peter Abeles
 */
public class LinearSolverCholLDL extends LinearSolverAbstract {

    private CholeskyDecompositionLDL decomp;
    private int n;
    private double vv[];
    private double el[];
    private double d[];

    public LinearSolverCholLDL( CholeskyDecompositionLDL decomp ) {
        this.decomp = decomp;
    }

    public LinearSolverCholLDL() {
        this.decomp = new CholeskyDecompositionLDL();
    }

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

        if( decomp.decompose(A) ){
            n = A.numCols;
            vv = decomp._getVV();
            el = decomp.getL().data;
            d = decomp.getD();
            return true;
        } else {
            return false;
        }
    }

    @Override
    public double quality() {
        return Math.abs(SpecializedOps.diagProd(decomp.getL()));
    }

    /**
     * 

* Using the decomposition, finds the value of 'X' in the linear equation below:
* * A*x = b
* * where A has dimension of n by n, x and b are n by m dimension. *

*

* *Note* that 'b' and 'x' can be the same matrix instance. *

* * @param B A matrix that is n by m. Not modified. * @param X An n by m matrix where the solution is writen to. Modified. */ @Override public void solve( DenseMatrix64F B , DenseMatrix64F X ) { if( B.numCols != X.numCols && B.numRows != n && X.numRows != n) { throw new IllegalArgumentException("Unexpected matrix size"); } int numCols = B.numCols; double dataB[] = B.data; double dataX[] = X.data; for( int j = 0; j < numCols; j++ ) { for( int i = 0; i < n; i++ ) vv[i] = dataB[i*numCols+j]; solveInternal(); for( int i = 0; i < n; i++ ) dataX[i*numCols+j] = vv[i]; } } /** * Used internally to find the solution to a single column vector. */ private void solveInternal() { // solve L*s=b storing y in x TriangularSolver.solveL(el,vv,n); // solve D*y=s for( int i = 0; i < n; i++ ) { vv[i] /= d[i]; } // solve L^T*x=y TriangularSolver.solveTranL(el,vv,n); } /** * Sets the matrix 'inv' equal to the inverse of the matrix that was decomposed. * * @param inv Where the value of the inverse will be stored. Modified. */ @Override public void invert( DenseMatrix64F inv ) { if( inv.numRows != n || inv.numCols != n ) { throw new RuntimeException("Unexpected matrix dimension"); } double a[] = inv.data; // solve L*z = b for( int i =0; i < n; i++ ) { for( int j = 0; j <= i; j++ ) { double sum = (i==j) ? 1.0 : 0.0; for( int k=i-1; k >=j; k-- ) { sum -= el[i*n+k]*a[j*n+k]; } a[j*n+i] = sum; } } // solve D*y=z for( int i =0; i < n; i++ ) { double inv_d = 1.0/d[i]; for( int j = 0; j <= i; j++ ) { a[j*n+i] *= inv_d; } } // solve L^T*x = y for( int i=n-1; i>=0; i-- ) { for( int j = 0; j <= i; j++ ) { double sum = (i




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