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Java example source code file (QRDecomposition.java)

This example Java source code file (QRDecomposition.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

arrayrealvector, blockrealmatrix, decompositionsolver, qrdecomposition, realmatrix, realvector, singularmatrixexception, solver, util

The QRDecomposition.java Java example source code

/*
 * 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.math3.linear;

import java.util.Arrays;

import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.util.FastMath;


/**
 * Calculates the QR-decomposition of a matrix.
 * <p>The QR-decomposition of a matrix A consists of two matrices Q and R
 * that satisfy: A = QR, Q is orthogonal (Q<sup>TQ = I), and R is
 * upper triangular. If A is m×n, Q is m×m and R m×n.</p>
 * <p>This class compute the decomposition using Householder reflectors.

* <p>For efficiency purposes, the decomposition in packed form is transposed. * This allows inner loop to iterate inside rows, which is much more cache-efficient * in Java.</p> * <p>This class is based on the class with similar name from the * <a href="http://math.nist.gov/javanumerics/jama/">JAMA library, with the * following changes:</p> * <ul> * <li>a {@link #getQT() getQT} method has been added, * <li>the {@code solve} and {@code isFullRank} methods have been replaced * by a {@link #getSolver() getSolver} method and the equivalent methods * provided by the returned {@link DecompositionSolver}.</li> * </ul> * * @see <a href="http://mathworld.wolfram.com/QRDecomposition.html">MathWorld * @see <a href="http://en.wikipedia.org/wiki/QR_decomposition">Wikipedia * * @since 1.2 (changed to concrete class in 3.0) */ public class QRDecomposition { /** * A packed TRANSPOSED representation of the QR decomposition. * <p>The elements BELOW the diagonal are the elements of the UPPER triangular * matrix R, and the rows ABOVE the diagonal are the Householder reflector vectors * from which an explicit form of Q can be recomputed if desired.</p> */ private double[][] qrt; /** The diagonal elements of R. */ private double[] rDiag; /** Cached value of Q. */ private RealMatrix cachedQ; /** Cached value of QT. */ private RealMatrix cachedQT; /** Cached value of R. */ private RealMatrix cachedR; /** Cached value of H. */ private RealMatrix cachedH; /** Singularity threshold. */ private final double threshold; /** * Calculates the QR-decomposition of the given matrix. * The singularity threshold defaults to zero. * * @param matrix The matrix to decompose. * * @see #QRDecomposition(RealMatrix,double) */ public QRDecomposition(RealMatrix matrix) { this(matrix, 0d); } /** * Calculates the QR-decomposition of the given matrix. * * @param matrix The matrix to decompose. * @param threshold Singularity threshold. */ public QRDecomposition(RealMatrix matrix, double threshold) { this.threshold = threshold; final int m = matrix.getRowDimension(); final int n = matrix.getColumnDimension(); qrt = matrix.transpose().getData(); rDiag = new double[FastMath.min(m, n)]; cachedQ = null; cachedQT = null; cachedR = null; cachedH = null; decompose(qrt); } /** Decompose matrix. * @param matrix transposed matrix * @since 3.2 */ protected void decompose(double[][] matrix) { for (int minor = 0; minor < FastMath.min(matrix.length, matrix[0].length); minor++) { performHouseholderReflection(minor, matrix); } } /** Perform Householder reflection for a minor A(minor, minor) of A. * @param minor minor index * @param matrix transposed matrix * @since 3.2 */ protected void performHouseholderReflection(int minor, double[][] matrix) { final double[] qrtMinor = matrix[minor]; /* * Let x be the first column of the minor, and a^2 = |x|^2. * x will be in the positions qr[minor][minor] through qr[m][minor]. * The first column of the transformed minor will be (a,0,0,..)' * The sign of a is chosen to be opposite to the sign of the first * component of x. Let's find a: */ double xNormSqr = 0; for (int row = minor; row < qrtMinor.length; row++) { final double c = qrtMinor[row]; xNormSqr += c * c; } final double a = (qrtMinor[minor] > 0) ? -FastMath.sqrt(xNormSqr) : FastMath.sqrt(xNormSqr); rDiag[minor] = a; if (a != 0.0) { /* * Calculate the normalized reflection vector v and transform * the first column. We know the norm of v beforehand: v = x-ae * so |v|^2 = <x-ae,x-ae> = -2a+a^2 = * a^2+a^2-2a<x,e> = 2a*(a - ). * Here <x, e> is now qr[minor][minor]. * v = x-ae is stored in the column at qr: */ qrtMinor[minor] -= a; // now |v|^2 = -2a*(qr[minor][minor]) /* * Transform the rest of the columns of the minor: * They will be transformed by the matrix H = I-2vv'/|v|^2. * If x is a column vector of the minor, then * Hx = (I-2vv'/|v|^2)x = x-2vv'x/|v|^2 = x - 2<x,v>/|v|^2 v. * Therefore the transformation is easily calculated by * subtracting the column vector (2<x,v>/|v|^2)v from x. * * Let 2<x,v>/|v|^2 = alpha. From above we have * |v|^2 = -2a*(qr[minor][minor]), so * alpha = -<x,v>/(a*qr[minor][minor]) */ for (int col = minor+1; col < matrix.length; col++) { final double[] qrtCol = matrix[col]; double alpha = 0; for (int row = minor; row < qrtCol.length; row++) { alpha -= qrtCol[row] * qrtMinor[row]; } alpha /= a * qrtMinor[minor]; // Subtract the column vector alpha*v from x. for (int row = minor; row < qrtCol.length; row++) { qrtCol[row] -= alpha * qrtMinor[row]; } } } } /** * Returns the matrix R of the decomposition. * <p>R is an upper-triangular matrix

* @return the R matrix */ public RealMatrix getR() { if (cachedR == null) { // R is supposed to be m x n final int n = qrt.length; final int m = qrt[0].length; double[][] ra = new double[m][n]; // copy the diagonal from rDiag and the upper triangle of qr for (int row = FastMath.min(m, n) - 1; row >= 0; row--) { ra[row][row] = rDiag[row]; for (int col = row + 1; col < n; col++) { ra[row][col] = qrt[col][row]; } } cachedR = MatrixUtils.createRealMatrix(ra); } // return the cached matrix return cachedR; } /** * Returns the matrix Q of the decomposition. * <p>Q is an orthogonal matrix

* @return the Q matrix */ public RealMatrix getQ() { if (cachedQ == null) { cachedQ = getQT().transpose(); } return cachedQ; } /** * Returns the transpose of the matrix Q of the decomposition. * <p>Q is an orthogonal matrix

* @return the transpose of the Q matrix, Q<sup>T */ public RealMatrix getQT() { if (cachedQT == null) { // QT is supposed to be m x m final int n = qrt.length; final int m = qrt[0].length; double[][] qta = new double[m][m]; /* * Q = Q1 Q2 ... Q_m, so Q is formed by first constructing Q_m and then * applying the Householder transformations Q_(m-1),Q_(m-2),...,Q1 in * succession to the result */ for (int minor = m - 1; minor >= FastMath.min(m, n); minor--) { qta[minor][minor] = 1.0d; } for (int minor = FastMath.min(m, n)-1; minor >= 0; minor--){ final double[] qrtMinor = qrt[minor]; qta[minor][minor] = 1.0d; if (qrtMinor[minor] != 0.0) { for (int col = minor; col < m; col++) { double alpha = 0; for (int row = minor; row < m; row++) { alpha -= qta[col][row] * qrtMinor[row]; } alpha /= rDiag[minor] * qrtMinor[minor]; for (int row = minor; row < m; row++) { qta[col][row] += -alpha * qrtMinor[row]; } } } } cachedQT = MatrixUtils.createRealMatrix(qta); } // return the cached matrix return cachedQT; } /** * Returns the Householder reflector vectors. * <p>H is a lower trapezoidal matrix whose columns represent * each successive Householder reflector vector. This matrix is used * to compute Q.</p> * @return a matrix containing the Householder reflector vectors */ public RealMatrix getH() { if (cachedH == null) { final int n = qrt.length; final int m = qrt[0].length; double[][] ha = new double[m][n]; for (int i = 0; i < m; ++i) { for (int j = 0; j < FastMath.min(i + 1, n); ++j) { ha[i][j] = qrt[j][i] / -rDiag[j]; } } cachedH = MatrixUtils.createRealMatrix(ha); } // return the cached matrix return cachedH; } /** * Get a solver for finding the A × X = B solution in least square sense. * <p> * Least Square sense means a solver can be computed for an overdetermined system, * (i.e. a system with more equations than unknowns, which corresponds to a tall A * matrix with more rows than columns). In any case, if the matrix is singular * within the tolerance set at {@link QRDecomposition#QRDecomposition(RealMatrix, * double) construction}, an error will be triggered when * the {@link DecompositionSolver#solve(RealVector) solve} method will be called. * </p> * @return a solver */ public DecompositionSolver getSolver() { return new Solver(qrt, rDiag, threshold); } /** Specialized solver. */ private static class Solver implements DecompositionSolver { /** * A packed TRANSPOSED representation of the QR decomposition. * <p>The elements BELOW the diagonal are the elements of the UPPER triangular * matrix R, and the rows ABOVE the diagonal are the Householder reflector vectors * from which an explicit form of Q can be recomputed if desired.</p> */ private final double[][] qrt; /** The diagonal elements of R. */ private final double[] rDiag; /** Singularity threshold. */ private final double threshold; /** * Build a solver from decomposed matrix. * * @param qrt Packed TRANSPOSED representation of the QR decomposition. * @param rDiag Diagonal elements of R. * @param threshold Singularity threshold. */ private Solver(final double[][] qrt, final double[] rDiag, final double threshold) { this.qrt = qrt; this.rDiag = rDiag; this.threshold = threshold; } /** {@inheritDoc} */ public boolean isNonSingular() { for (double diag : rDiag) { if (FastMath.abs(diag) <= threshold) { return false; } } return true; } /** {@inheritDoc} */ public RealVector solve(RealVector b) { final int n = qrt.length; final int m = qrt[0].length; if (b.getDimension() != m) { throw new DimensionMismatchException(b.getDimension(), m); } if (!isNonSingular()) { throw new SingularMatrixException(); } final double[] x = new double[n]; final double[] y = b.toArray(); // apply Householder transforms to solve Q.y = b for (int minor = 0; minor < FastMath.min(m, n); minor++) { final double[] qrtMinor = qrt[minor]; double dotProduct = 0; for (int row = minor; row < m; row++) { dotProduct += y[row] * qrtMinor[row]; } dotProduct /= rDiag[minor] * qrtMinor[minor]; for (int row = minor; row < m; row++) { y[row] += dotProduct * qrtMinor[row]; } } // solve triangular system R.x = y for (int row = rDiag.length - 1; row >= 0; --row) { y[row] /= rDiag[row]; final double yRow = y[row]; final double[] qrtRow = qrt[row]; x[row] = yRow; for (int i = 0; i < row; i++) { y[i] -= yRow * qrtRow[i]; } } return new ArrayRealVector(x, false); } /** {@inheritDoc} */ public RealMatrix solve(RealMatrix b) { final int n = qrt.length; final int m = qrt[0].length; if (b.getRowDimension() != m) { throw new DimensionMismatchException(b.getRowDimension(), m); } if (!isNonSingular()) { throw new SingularMatrixException(); } final int columns = b.getColumnDimension(); final int blockSize = BlockRealMatrix.BLOCK_SIZE; final int cBlocks = (columns + blockSize - 1) / blockSize; final double[][] xBlocks = BlockRealMatrix.createBlocksLayout(n, columns); final double[][] y = new double[b.getRowDimension()][blockSize]; final double[] alpha = new double[blockSize]; for (int kBlock = 0; kBlock < cBlocks; ++kBlock) { final int kStart = kBlock * blockSize; final int kEnd = FastMath.min(kStart + blockSize, columns); final int kWidth = kEnd - kStart; // get the right hand side vector b.copySubMatrix(0, m - 1, kStart, kEnd - 1, y); // apply Householder transforms to solve Q.y = b for (int minor = 0; minor < FastMath.min(m, n); minor++) { final double[] qrtMinor = qrt[minor]; final double factor = 1.0 / (rDiag[minor] * qrtMinor[minor]); Arrays.fill(alpha, 0, kWidth, 0.0); for (int row = minor; row < m; ++row) { final double d = qrtMinor[row]; final double[] yRow = y[row]; for (int k = 0; k < kWidth; ++k) { alpha[k] += d * yRow[k]; } } for (int k = 0; k < kWidth; ++k) { alpha[k] *= factor; } for (int row = minor; row < m; ++row) { final double d = qrtMinor[row]; final double[] yRow = y[row]; for (int k = 0; k < kWidth; ++k) { yRow[k] += alpha[k] * d; } } } // solve triangular system R.x = y for (int j = rDiag.length - 1; j >= 0; --j) { final int jBlock = j / blockSize; final int jStart = jBlock * blockSize; final double factor = 1.0 / rDiag[j]; final double[] yJ = y[j]; final double[] xBlock = xBlocks[jBlock * cBlocks + kBlock]; int index = (j - jStart) * kWidth; for (int k = 0; k < kWidth; ++k) { yJ[k] *= factor; xBlock[index++] = yJ[k]; } final double[] qrtJ = qrt[j]; for (int i = 0; i < j; ++i) { final double rIJ = qrtJ[i]; final double[] yI = y[i]; for (int k = 0; k < kWidth; ++k) { yI[k] -= yJ[k] * rIJ; } } } } return new BlockRealMatrix(n, columns, xBlocks, false); } /** * {@inheritDoc} * @throws SingularMatrixException if the decomposed matrix is singular. */ public RealMatrix getInverse() { return solve(MatrixUtils.createRealIdentityMatrix(qrt[0].length)); } } }

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