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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 org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.Precision;
/**
* Class transforming a general real matrix to Schur form.
* A m × m matrix A can be written as the product of three matrices: A = P
* × T × PT with P an orthogonal matrix and T an quasi-triangular
* matrix. Both P and T are m × m matrices.
* Transformation to Schur form 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.
* This class is based on the method hqr2 in class EigenvalueDecomposition
* from the JAMA library.
*
* @see Schur Decomposition - MathWorld
* @see Schur Decomposition - Wikipedia
* @see Householder Transformations
* @version $Id: SchurTransformer.java 1389129 2012-09-23 19:34:02Z tn $
* @since 3.1
*/
class SchurTransformer {
/** Maximum allowed iterations for convergence of the transformation. */
private static final int MAX_ITERATIONS = 100;
/** P matrix. */
private final double matrixP[][];
/** T matrix. */
private final double matrixT[][];
/** Cached value of P. */
private RealMatrix cachedP;
/** Cached value of T. */
private RealMatrix cachedT;
/** Cached value of PT. */
private RealMatrix cachedPt;
/** Epsilon criteria taken from JAMA code (originally was 2^-52). */
private final double epsilon = Precision.EPSILON;
/**
* Build the transformation to Schur form of a general real matrix.
*
* @param matrix matrix to transform
* @throws NonSquareMatrixException if the matrix is not square
*/
public SchurTransformer(final RealMatrix matrix) {
if (!matrix.isSquare()) {
throw new NonSquareMatrixException(matrix.getRowDimension(),
matrix.getColumnDimension());
}
HessenbergTransformer transformer = new HessenbergTransformer(matrix);
matrixT = transformer.getH().getData();
matrixP = transformer.getP().getData();
cachedT = null;
cachedP = null;
cachedPt = null;
// transform matrix
transform();
}
/**
* Returns the matrix P of the transform.
* P is an orthogonal matrix, i.e. its inverse is also its transpose.
*
* @return the P matrix
*/
public RealMatrix getP() {
if (cachedP == null) {
cachedP = MatrixUtils.createRealMatrix(matrixP);
}
return cachedP;
}
/**
* Returns the transpose of the matrix P of the transform.
* P is an orthogonal matrix, i.e. its inverse is also its transpose.
*
* @return the transpose of the P matrix
*/
public RealMatrix getPT() {
if (cachedPt == null) {
cachedPt = getP().transpose();
}
// return the cached matrix
return cachedPt;
}
/**
* Returns the quasi-triangular Schur matrix T of the transform.
*
* @return the T matrix
*/
public RealMatrix getT() {
if (cachedT == null) {
cachedT = MatrixUtils.createRealMatrix(matrixT);
}
// return the cached matrix
return cachedT;
}
/**
* Transform original matrix to Schur form.
* @throws MaxCountExceededException if the transformation does not converge
*/
private void transform() {
final int n = matrixT.length;
// compute matrix norm
final double norm = getNorm();
// shift information
final ShiftInfo shift = new ShiftInfo();
// Outer loop over eigenvalue index
int iteration = 0;
int iu = n - 1;
while (iu >= 0) {
// Look for single small sub-diagonal element
final int il = findSmallSubDiagonalElement(iu, norm);
// Check for convergence
if (il == iu) {
// One root found
matrixT[iu][iu] = matrixT[iu][iu] + shift.exShift;
iu--;
iteration = 0;
} else if (il == iu - 1) {
// Two roots found
double p = (matrixT[iu - 1][iu - 1] - matrixT[iu][iu]) / 2.0;
double q = p * p + matrixT[iu][iu - 1] * matrixT[iu - 1][iu];
matrixT[iu][iu] += shift.exShift;
matrixT[iu - 1][iu - 1] += shift.exShift;
if (q >= 0) {
double z = FastMath.sqrt(FastMath.abs(q));
if (p >= 0) {
z = p + z;
} else {
z = p - z;
}
final double x = matrixT[iu][iu - 1];
final double s = FastMath.abs(x) + FastMath.abs(z);
p = x / s;
q = z / s;
final double r = FastMath.sqrt(p * p + q * q);
p = p / r;
q = q / r;
// Row modification
for (int j = iu - 1; j < n; j++) {
z = matrixT[iu - 1][j];
matrixT[iu - 1][j] = q * z + p * matrixT[iu][j];
matrixT[iu][j] = q * matrixT[iu][j] - p * z;
}
// Column modification
for (int i = 0; i <= iu; i++) {
z = matrixT[i][iu - 1];
matrixT[i][iu - 1] = q * z + p * matrixT[i][iu];
matrixT[i][iu] = q * matrixT[i][iu] - p * z;
}
// Accumulate transformations
for (int i = 0; i <= n - 1; i++) {
z = matrixP[i][iu - 1];
matrixP[i][iu - 1] = q * z + p * matrixP[i][iu];
matrixP[i][iu] = q * matrixP[i][iu] - p * z;
}
}
iu -= 2;
iteration = 0;
} else {
// No convergence yet
computeShift(il, iu, iteration, shift);
// stop transformation after too many iterations
if (++iteration > MAX_ITERATIONS) {
throw new MaxCountExceededException(LocalizedFormats.CONVERGENCE_FAILED,
MAX_ITERATIONS);
}
// the initial houseHolder vector for the QR step
final double[] hVec = new double[3];
final int im = initQRStep(il, iu, shift, hVec);
performDoubleQRStep(il, im, iu, shift, hVec);
}
}
}
/**
* Computes the L1 norm of the (quasi-)triangular matrix T.
*
* @return the L1 norm of matrix T
*/
private double getNorm() {
double norm = 0.0;
for (int i = 0; i < matrixT.length; i++) {
// as matrix T is (quasi-)triangular, also take the sub-diagonal element into account
for (int j = FastMath.max(i - 1, 0); j < matrixT.length; j++) {
norm += FastMath.abs(matrixT[i][j]);
}
}
return norm;
}
/**
* Find the first small sub-diagonal element and returns its index.
*
* @param startIdx the starting index for the search
* @param norm the L1 norm of the matrix
* @return the index of the first small sub-diagonal element
*/
private int findSmallSubDiagonalElement(final int startIdx, final double norm) {
int l = startIdx;
while (l > 0) {
double s = FastMath.abs(matrixT[l - 1][l - 1]) + FastMath.abs(matrixT[l][l]);
if (s == 0.0) {
s = norm;
}
if (FastMath.abs(matrixT[l][l - 1]) < epsilon * s) {
break;
}
l--;
}
return l;
}
/**
* Compute the shift for the current iteration.
*
* @param l the index of the small sub-diagonal element
* @param idx the current eigenvalue index
* @param iteration the current iteration
* @param shift holder for shift information
*/
private void computeShift(final int l, final int idx, final int iteration, final ShiftInfo shift) {
// Form shift
shift.x = matrixT[idx][idx];
shift.y = shift.w = 0.0;
if (l < idx) {
shift.y = matrixT[idx - 1][idx - 1];
shift.w = matrixT[idx][idx - 1] * matrixT[idx - 1][idx];
}
// Wilkinson's original ad hoc shift
if (iteration == 10) {
shift.exShift += shift.x;
for (int i = 0; i <= idx; i++) {
matrixT[i][i] -= shift.x;
}
final double s = FastMath.abs(matrixT[idx][idx - 1]) + FastMath.abs(matrixT[idx - 1][idx - 2]);
shift.x = 0.75 * s;
shift.y = 0.75 * s;
shift.w = -0.4375 * s * s;
}
// MATLAB's new ad hoc shift
if (iteration == 30) {
double s = (shift.y - shift.x) / 2.0;
s = s * s + shift.w;
if (s > 0.0) {
s = FastMath.sqrt(s);
if (shift.y < shift.x) {
s = -s;
}
s = shift.x - shift.w / ((shift.y - shift.x) / 2.0 + s);
for (int i = 0; i <= idx; i++) {
matrixT[i][i] -= s;
}
shift.exShift += s;
shift.x = shift.y = shift.w = 0.964;
}
}
}
/**
* Initialize the householder vectors for the QR step.
*
* @param il the index of the small sub-diagonal element
* @param iu the current eigenvalue index
* @param shift shift information holder
* @param hVec the initial houseHolder vector
* @return the start index for the QR step
*/
private int initQRStep(int il, final int iu, final ShiftInfo shift, double[] hVec) {
// Look for two consecutive small sub-diagonal elements
int im = iu - 2;
while (im >= il) {
final double z = matrixT[im][im];
final double r = shift.x - z;
double s = shift.y - z;
hVec[0] = (r * s - shift.w) / matrixT[im + 1][im] + matrixT[im][im + 1];
hVec[1] = matrixT[im + 1][im + 1] - z - r - s;
hVec[2] = matrixT[im + 2][im + 1];
if (im == il) {
break;
}
final double lhs = FastMath.abs(matrixT[im][im - 1]) * (FastMath.abs(hVec[1]) + FastMath.abs(hVec[2]));
final double rhs = FastMath.abs(hVec[0]) * (FastMath.abs(matrixT[im - 1][im - 1]) +
FastMath.abs(z) +
FastMath.abs(matrixT[im + 1][im + 1]));
if (lhs < epsilon * rhs) {
break;
}
im--;
}
return im;
}
/**
* Perform a double QR step involving rows l:idx and columns m:n
*
* @param il the index of the small sub-diagonal element
* @param im the start index for the QR step
* @param iu the current eigenvalue index
* @param shift shift information holder
* @param hVec the initial houseHolder vector
*/
private void performDoubleQRStep(final int il, final int im, final int iu,
final ShiftInfo shift, final double[] hVec) {
final int n = matrixT.length;
double p = hVec[0];
double q = hVec[1];
double r = hVec[2];
for (int k = im; k <= iu - 1; k++) {
boolean notlast = k != (iu - 1);
if (k != im) {
p = matrixT[k][k - 1];
q = matrixT[k + 1][k - 1];
r = notlast ? matrixT[k + 2][k - 1] : 0.0;
shift.x = FastMath.abs(p) + FastMath.abs(q) + FastMath.abs(r);
if (!Precision.equals(shift.x, 0.0, epsilon)) {
p = p / shift.x;
q = q / shift.x;
r = r / shift.x;
}
}
if (shift.x == 0.0) {
break;
}
double s = FastMath.sqrt(p * p + q * q + r * r);
if (p < 0.0) {
s = -s;
}
if (s != 0.0) {
if (k != im) {
matrixT[k][k - 1] = -s * shift.x;
} else if (il != im) {
matrixT[k][k - 1] = -matrixT[k][k - 1];
}
p = p + s;
shift.x = p / s;
shift.y = q / s;
double z = r / s;
q = q / p;
r = r / p;
// Row modification
for (int j = k; j < n; j++) {
p = matrixT[k][j] + q * matrixT[k + 1][j];
if (notlast) {
p = p + r * matrixT[k + 2][j];
matrixT[k + 2][j] = matrixT[k + 2][j] - p * z;
}
matrixT[k][j] = matrixT[k][j] - p * shift.x;
matrixT[k + 1][j] = matrixT[k + 1][j] - p * shift.y;
}
// Column modification
for (int i = 0; i <= FastMath.min(iu, k + 3); i++) {
p = shift.x * matrixT[i][k] + shift.y * matrixT[i][k + 1];
if (notlast) {
p = p + z * matrixT[i][k + 2];
matrixT[i][k + 2] = matrixT[i][k + 2] - p * r;
}
matrixT[i][k] = matrixT[i][k] - p;
matrixT[i][k + 1] = matrixT[i][k + 1] - p * q;
}
// Accumulate transformations
final int high = matrixT.length - 1;
for (int i = 0; i <= high; i++) {
p = shift.x * matrixP[i][k] + shift.y * matrixP[i][k + 1];
if (notlast) {
p = p + z * matrixP[i][k + 2];
matrixP[i][k + 2] = matrixP[i][k + 2] - p * r;
}
matrixP[i][k] = matrixP[i][k] - p;
matrixP[i][k + 1] = matrixP[i][k + 1] - p * q;
}
} // (s != 0)
} // k loop
// clean up pollution due to round-off errors
for (int i = im + 2; i <= iu; i++) {
matrixT[i][i-2] = 0.0;
if (i > im + 2) {
matrixT[i][i-3] = 0.0;
}
}
}
/**
* Internal data structure holding the current shift information.
* Contains variable names as present in the original JAMA code.
*/
private static class ShiftInfo {
// CHECKSTYLE: stop all
/** x shift info */
double x;
/** y shift info */
double y;
/** w shift info */
double w;
/** Indicates an exceptional shift. */
double exShift;
// CHECKSTYLE: resume all
}
}