Java tutorial
/* * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with * this work for additional information regarding copyright ownership. * The ASF licenses this file to You under the Apache License, Version 2.0 * (the "License"); you may not use this file except in compliance with * the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.apache.commons.math.linear; import org.apache.commons.math.MathRuntimeException; import org.apache.commons.math.exception.util.LocalizedFormats; import org.apache.commons.math.util.FastMath; /** * Calculates the Cholesky decomposition of a matrix. * <p>The Cholesky decomposition of a real symmetric positive-definite * matrix A consists of a lower triangular matrix L with same size that * satisfy: A = LL<sup>T</sup>Q = I). In a sense, this is the square root of A.</p> * * @see <a href="http://mathworld.wolfram.com/CholeskyDecomposition.html">MathWorld</a> * @see <a href="http://en.wikipedia.org/wiki/Cholesky_decomposition">Wikipedia</a> * @version $Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 aot 2010) $ * @since 2.0 */ public class CholeskyDecompositionImpl implements CholeskyDecomposition { /** Default threshold above which off-diagonal elements are considered too different * and matrix not symmetric. */ public static final double DEFAULT_RELATIVE_SYMMETRY_THRESHOLD = 1.0e-15; /** Default threshold below which diagonal elements are considered null * and matrix not positive definite. */ public static final double DEFAULT_ABSOLUTE_POSITIVITY_THRESHOLD = 1.0e-10; /** Row-oriented storage for L<sup>T</sup> matrix data. */ private double[][] lTData; /** Cached value of L. */ private RealMatrix cachedL; /** Cached value of LT. */ private RealMatrix cachedLT; /** * Calculates the Cholesky decomposition of the given matrix. * <p> * Calling this constructor is equivalent to call {@link * #CholeskyDecompositionImpl(RealMatrix, double, double)} with the * thresholds set to the default values {@link * #DEFAULT_RELATIVE_SYMMETRY_THRESHOLD} and {@link * #DEFAULT_ABSOLUTE_POSITIVITY_THRESHOLD} * </p> * @param matrix the matrix to decompose * @exception NonSquareMatrixException if matrix is not square * @exception NotSymmetricMatrixException if matrix is not symmetric * @exception NotPositiveDefiniteMatrixException if the matrix is not * strictly positive definite * @see #CholeskyDecompositionImpl(RealMatrix, double, double) * @see #DEFAULT_RELATIVE_SYMMETRY_THRESHOLD * @see #DEFAULT_ABSOLUTE_POSITIVITY_THRESHOLD */ public CholeskyDecompositionImpl(final RealMatrix matrix) throws NonSquareMatrixException, NotSymmetricMatrixException, NotPositiveDefiniteMatrixException { this(matrix, DEFAULT_RELATIVE_SYMMETRY_THRESHOLD, DEFAULT_ABSOLUTE_POSITIVITY_THRESHOLD); } /** * Calculates the Cholesky decomposition of the given matrix. * @param matrix the matrix to decompose * @param relativeSymmetryThreshold threshold above which off-diagonal * elements are considered too different and matrix not symmetric * @param absolutePositivityThreshold threshold below which diagonal * elements are considered null and matrix not positive definite * @exception NonSquareMatrixException if matrix is not square * @exception NotSymmetricMatrixException if matrix is not symmetric * @exception NotPositiveDefiniteMatrixException if the matrix is not * strictly positive definite * @see #CholeskyDecompositionImpl(RealMatrix) * @see #DEFAULT_RELATIVE_SYMMETRY_THRESHOLD * @see #DEFAULT_ABSOLUTE_POSITIVITY_THRESHOLD */ public CholeskyDecompositionImpl(final RealMatrix matrix, final double relativeSymmetryThreshold, final double absolutePositivityThreshold) throws NonSquareMatrixException, NotSymmetricMatrixException, NotPositiveDefiniteMatrixException { if (!matrix.isSquare()) { throw new NonSquareMatrixException(matrix.getRowDimension(), matrix.getColumnDimension()); } final int order = matrix.getRowDimension(); lTData = matrix.getData(); cachedL = null; cachedLT = null; // check the matrix before transformation for (int i = 0; i < order; ++i) { final double[] lI = lTData[i]; // check off-diagonal elements (and reset them to 0) for (int j = i + 1; j < order; ++j) { final double[] lJ = lTData[j]; final double lIJ = lI[j]; final double lJI = lJ[i]; final double maxDelta = relativeSymmetryThreshold * FastMath.max(FastMath.abs(lIJ), FastMath.abs(lJI)); if (FastMath.abs(lIJ - lJI) > maxDelta) { throw new NotSymmetricMatrixException(); } lJ[i] = 0; } } // transform the matrix for (int i = 0; i < order; ++i) { final double[] ltI = lTData[i]; // check diagonal element if (ltI[i] < absolutePositivityThreshold) { throw new NotPositiveDefiniteMatrixException(); } ltI[i] = FastMath.sqrt(ltI[i]); final double inverse = 1.0 / ltI[i]; for (int q = order - 1; q > i; --q) { ltI[q] *= inverse; final double[] ltQ = lTData[q]; for (int p = q; p < order; ++p) { ltQ[p] -= ltI[q] * ltI[p]; } } } } /** {@inheritDoc} */ public RealMatrix getL() { if (cachedL == null) { cachedL = getLT().transpose(); } return cachedL; } /** {@inheritDoc} */ public RealMatrix getLT() { if (cachedLT == null) { cachedLT = MatrixUtils.createRealMatrix(lTData); } // return the cached matrix return cachedLT; } /** {@inheritDoc} */ public double getDeterminant() { double determinant = 1.0; for (int i = 0; i < lTData.length; ++i) { double lTii = lTData[i][i]; determinant *= lTii * lTii; } return determinant; } /** {@inheritDoc} */ public DecompositionSolver getSolver() { return new Solver(lTData); } /** Specialized solver. */ private static class Solver implements DecompositionSolver { /** Row-oriented storage for L<sup>T</sup> matrix data. */ private final double[][] lTData; /** * Build a solver from decomposed matrix. * @param lTData row-oriented storage for L<sup>T</sup> matrix data */ private Solver(final double[][] lTData) { this.lTData = lTData; } /** {@inheritDoc} */ public boolean isNonSingular() { // if we get this far, the matrix was positive definite, hence non-singular return true; } /** {@inheritDoc} */ public double[] solve(double[] b) throws IllegalArgumentException, InvalidMatrixException { final int m = lTData.length; if (b.length != m) { throw MathRuntimeException.createIllegalArgumentException(LocalizedFormats.VECTOR_LENGTH_MISMATCH, b.length, m); } final double[] x = b.clone(); // Solve LY = b for (int j = 0; j < m; j++) { final double[] lJ = lTData[j]; x[j] /= lJ[j]; final double xJ = x[j]; for (int i = j + 1; i < m; i++) { x[i] -= xJ * lJ[i]; } } // Solve LTX = Y for (int j = m - 1; j >= 0; j--) { x[j] /= lTData[j][j]; final double xJ = x[j]; for (int i = 0; i < j; i++) { x[i] -= xJ * lTData[i][j]; } } return x; } /** {@inheritDoc} */ public RealVector solve(RealVector b) throws IllegalArgumentException, InvalidMatrixException { try { return solve((ArrayRealVector) b); } catch (ClassCastException cce) { final int m = lTData.length; if (b.getDimension() != m) { throw MathRuntimeException.createIllegalArgumentException( LocalizedFormats.VECTOR_LENGTH_MISMATCH, b.getDimension(), m); } final double[] x = b.getData(); // Solve LY = b for (int j = 0; j < m; j++) { final double[] lJ = lTData[j]; x[j] /= lJ[j]; final double xJ = x[j]; for (int i = j + 1; i < m; i++) { x[i] -= xJ * lJ[i]; } } // Solve LTX = Y for (int j = m - 1; j >= 0; j--) { x[j] /= lTData[j][j]; final double xJ = x[j]; for (int i = 0; i < j; i++) { x[i] -= xJ * lTData[i][j]; } } return new ArrayRealVector(x, false); } } /** Solve the linear equation A × X = B. * <p>The A matrix is implicit here. It is </p> * @param b right-hand side of the equation A × X = B * @return a vector X such that A × X = B * @exception IllegalArgumentException if matrices dimensions don't match * @exception InvalidMatrixException if decomposed matrix is singular */ public ArrayRealVector solve(ArrayRealVector b) throws IllegalArgumentException, InvalidMatrixException { return new ArrayRealVector(solve(b.getDataRef()), false); } /** {@inheritDoc} */ public RealMatrix solve(RealMatrix b) throws IllegalArgumentException, InvalidMatrixException { final int m = lTData.length; if (b.getRowDimension() != m) { throw MathRuntimeException.createIllegalArgumentException(LocalizedFormats.DIMENSIONS_MISMATCH_2x2, b.getRowDimension(), b.getColumnDimension(), m, "n"); } final int nColB = b.getColumnDimension(); double[][] x = b.getData(); // Solve LY = b for (int j = 0; j < m; j++) { final double[] lJ = lTData[j]; final double lJJ = lJ[j]; final double[] xJ = x[j]; for (int k = 0; k < nColB; ++k) { xJ[k] /= lJJ; } for (int i = j + 1; i < m; i++) { final double[] xI = x[i]; final double lJI = lJ[i]; for (int k = 0; k < nColB; ++k) { xI[k] -= xJ[k] * lJI; } } } // Solve LTX = Y for (int j = m - 1; j >= 0; j--) { final double lJJ = lTData[j][j]; final double[] xJ = x[j]; for (int k = 0; k < nColB; ++k) { xJ[k] /= lJJ; } for (int i = 0; i < j; i++) { final double[] xI = x[i]; final double lIJ = lTData[i][j]; for (int k = 0; k < nColB; ++k) { xI[k] -= xJ[k] * lIJ; } } } return new Array2DRowRealMatrix(x, false); } /** {@inheritDoc} */ public RealMatrix getInverse() throws InvalidMatrixException { return solve(MatrixUtils.createRealIdentityMatrix(lTData.length)); } } }