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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* 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.util.FastMath;
/**
* Class transforming a symmetrical matrix to tridiagonal shape.
* A symmetrical m × m matrix A can be written as the product of three matrices:
* A = Q × T × QT with Q an orthogonal matrix and T a symmetrical
* tridiagonal matrix. Both Q and T are m × m matrices.
* This implementation only uses the upper part of the matrix, the part below the
* diagonal is not accessed at all.
* Transformation to tridiagonal shape is often not a goal by itself, but it is
* an intermediate step in more general decomposition algorithms like {@link
* EigenDecomposition eigen decomposition}. This class is therefore intended for internal
* use by the library and is not public. As a consequence of this explicitly limited scope,
* many methods directly returns references to internal arrays, not copies.
* @since 2.0
*/
class TriDiagonalTransformer {
/** Householder vectors. */
private final double householderVectors[][];
/** Main diagonal. */
private final double[] main;
/** Secondary diagonal. */
private final double[] secondary;
/** Cached value of Q. */
private RealMatrix cachedQ;
/** Cached value of Qt. */
private RealMatrix cachedQt;
/** Cached value of T. */
private RealMatrix cachedT;
/**
* Build the transformation to tridiagonal shape of a symmetrical matrix.
* The specified matrix is assumed to be symmetrical without any check.
* Only the upper triangular part of the matrix is used.
*
* @param matrix Symmetrical matrix to transform.
* @throws NonSquareMatrixException if the matrix is not square.
*/
public TriDiagonalTransformer(RealMatrix matrix) {
if (!matrix.isSquare()) {
throw new NonSquareMatrixException(matrix.getRowDimension(),
matrix.getColumnDimension());
}
final int m = matrix.getRowDimension();
householderVectors = matrix.getData();
main = new double[m];
secondary = new double[m - 1];
cachedQ = null;
cachedQt = null;
cachedT = null;
// transform matrix
transform();
}
/**
* Returns the matrix Q of the transform.
* Q is an orthogonal matrix, i.e. its transpose is also its inverse.
* @return the Q matrix
*/
public RealMatrix getQ() {
if (cachedQ == null) {
cachedQ = getQT().transpose();
}
return cachedQ;
}
/**
* Returns the transpose of the matrix Q of the transform.
* Q is an orthogonal matrix, i.e. its transpose is also its inverse.
* @return the Q matrix
*/
public RealMatrix getQT() {
if (cachedQt == null) {
final int m = householderVectors.length;
double[][] qta = new double[m][m];
// build up first part of the matrix by applying Householder transforms
for (int k = m - 1; k >= 1; --k) {
final double[] hK = householderVectors[k - 1];
qta[k][k] = 1;
if (hK[k] != 0.0) {
final double inv = 1.0 / (secondary[k - 1] * hK[k]);
double beta = 1.0 / secondary[k - 1];
qta[k][k] = 1 + beta * hK[k];
for (int i = k + 1; i < m; ++i) {
qta[k][i] = beta * hK[i];
}
for (int j = k + 1; j < m; ++j) {
beta = 0;
for (int i = k + 1; i < m; ++i) {
beta += qta[j][i] * hK[i];
}
beta *= inv;
qta[j][k] = beta * hK[k];
for (int i = k + 1; i < m; ++i) {
qta[j][i] += beta * hK[i];
}
}
}
}
qta[0][0] = 1;
cachedQt = MatrixUtils.createRealMatrix(qta);
}
// return the cached matrix
return cachedQt;
}
/**
* Returns the tridiagonal matrix T of the transform.
* @return the T matrix
*/
public RealMatrix getT() {
if (cachedT == null) {
final int m = main.length;
double[][] ta = new double[m][m];
for (int i = 0; i < m; ++i) {
ta[i][i] = main[i];
if (i > 0) {
ta[i][i - 1] = secondary[i - 1];
}
if (i < main.length - 1) {
ta[i][i + 1] = secondary[i];
}
}
cachedT = MatrixUtils.createRealMatrix(ta);
}
// return the cached matrix
return cachedT;
}
/**
* Get the Householder vectors of the transform.
* Note that since this class is only intended for internal use,
* it returns directly a reference to its internal arrays, not a copy.
* @return the main diagonal elements of the B matrix
*/
double[][] getHouseholderVectorsRef() {
return householderVectors;
}
/**
* Get the main diagonal elements of the matrix T of the transform.
* Note that since this class is only intended for internal use,
* it returns directly a reference to its internal arrays, not a copy.
* @return the main diagonal elements of the T matrix
*/
double[] getMainDiagonalRef() {
return main;
}
/**
* Get the secondary diagonal elements of the matrix T of the transform.
* Note that since this class is only intended for internal use,
* it returns directly a reference to its internal arrays, not a copy.
* @return the secondary diagonal elements of the T matrix
*/
double[] getSecondaryDiagonalRef() {
return secondary;
}
/**
* Transform original matrix to tridiagonal form.
* Transformation is done using Householder transforms.
*/
private void transform() {
final int m = householderVectors.length;
final double[] z = new double[m];
for (int k = 0; k < m - 1; k++) {
//zero-out a row and a column simultaneously
final double[] hK = householderVectors[k];
main[k] = hK[k];
double xNormSqr = 0;
for (int j = k + 1; j < m; ++j) {
final double c = hK[j];
xNormSqr += c * c;
}
final double a = (hK[k + 1] > 0) ? -FastMath.sqrt(xNormSqr) : FastMath.sqrt(xNormSqr);
secondary[k] = a;
if (a != 0.0) {
// apply Householder transform from left and right simultaneously
hK[k + 1] -= a;
final double beta = -1 / (a * hK[k + 1]);
// compute a = beta A v, where v is the Householder vector
// this loop is written in such a way
// 1) only the upper triangular part of the matrix is accessed
// 2) access is cache-friendly for a matrix stored in rows
Arrays.fill(z, k + 1, m, 0);
for (int i = k + 1; i < m; ++i) {
final double[] hI = householderVectors[i];
final double hKI = hK[i];
double zI = hI[i] * hKI;
for (int j = i + 1; j < m; ++j) {
final double hIJ = hI[j];
zI += hIJ * hK[j];
z[j] += hIJ * hKI;
}
z[i] = beta * (z[i] + zI);
}
// compute gamma = beta vT z / 2
double gamma = 0;
for (int i = k + 1; i < m; ++i) {
gamma += z[i] * hK[i];
}
gamma *= beta / 2;
// compute z = z - gamma v
for (int i = k + 1; i < m; ++i) {
z[i] -= gamma * hK[i];
}
// update matrix: A = A - v zT - z vT
// only the upper triangular part of the matrix is updated
for (int i = k + 1; i < m; ++i) {
final double[] hI = householderVectors[i];
for (int j = i; j < m; ++j) {
hI[j] -= hK[i] * z[j] + z[i] * hK[j];
}
}
}
}
main[m - 1] = householderVectors[m - 1][m - 1];
}
}