org.apache.commons.math3.linear.RRQRDecomposition Maven / Gradle / Ivy
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* 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.util.FastMath;
/**
* Calculates the rank-revealing QR-decomposition of a matrix, with column pivoting.
* The rank-revealing QR-decomposition of a matrix A consists of three matrices Q,
* R and P such that AP=QR. Q is orthogonal (QTQ = I), and R is upper triangular.
* If A is m×n, Q is m×m and R is m×n and P is n×n.
* QR decomposition with column pivoting produces a rank-revealing QR
* decomposition and the {@link #getRank(double)} method may be used to return the rank of the
* input matrix A.
* This class compute the decomposition using Householder reflectors.
* 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.
* This class is based on the class with similar name from the
* JAMA library, with the
* following changes:
*
* - a {@link #getQT() getQT} method has been added,
* - 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}.
*
*
* @see MathWorld
* @see Wikipedia
*
* @since 3.2
*/
public class RRQRDecomposition extends QRDecomposition {
/** An array to record the column pivoting for later creation of P. */
private int[] p;
/** Cached value of P. */
private RealMatrix cachedP;
/**
* Calculates the QR-decomposition of the given matrix.
* The singularity threshold defaults to zero.
*
* @param matrix The matrix to decompose.
*
* @see #RRQRDecomposition(RealMatrix, double)
*/
public RRQRDecomposition(RealMatrix matrix) {
this(matrix, 0d);
}
/**
* Calculates the QR-decomposition of the given matrix.
*
* @param matrix The matrix to decompose.
* @param threshold Singularity threshold.
* @see #RRQRDecomposition(RealMatrix)
*/
public RRQRDecomposition(RealMatrix matrix, double threshold) {
super(matrix, threshold);
}
/** Decompose matrix.
* @param qrt transposed matrix
*/
@Override
protected void decompose(double[][] qrt) {
p = new int[qrt.length];
for (int i = 0; i < p.length; i++) {
p[i] = i;
}
super.decompose(qrt);
}
/** Perform Householder reflection for a minor A(minor, minor) of A.
* @param minor minor index
* @param qrt transposed matrix
*/
@Override
protected void performHouseholderReflection(int minor, double[][] qrt) {
double l2NormSquaredMax = 0;
// Find the unreduced column with the greatest L2-Norm
int l2NormSquaredMaxIndex = minor;
for (int i = minor; i < qrt.length; i++) {
double l2NormSquared = 0;
for (int j = 0; j < qrt[i].length; j++) {
l2NormSquared += qrt[i][j] * qrt[i][j];
}
if (l2NormSquared > l2NormSquaredMax) {
l2NormSquaredMax = l2NormSquared;
l2NormSquaredMaxIndex = i;
}
}
// swap the current column with that with the greated L2-Norm and record in p
if (l2NormSquaredMaxIndex != minor) {
double[] tmp1 = qrt[minor];
qrt[minor] = qrt[l2NormSquaredMaxIndex];
qrt[l2NormSquaredMaxIndex] = tmp1;
int tmp2 = p[minor];
p[minor] = p[l2NormSquaredMaxIndex];
p[l2NormSquaredMaxIndex] = tmp2;
}
super.performHouseholderReflection(minor, qrt);
}
/**
* Returns the pivot matrix, P, used in the QR Decomposition of matrix A such that AP = QR.
*
* If no pivoting is used in this decomposition then P is equal to the identity matrix.
*
* @return a permutation matrix.
*/
public RealMatrix getP() {
if (cachedP == null) {
int n = p.length;
cachedP = MatrixUtils.createRealMatrix(n,n);
for (int i = 0; i < n; i++) {
cachedP.setEntry(p[i], i, 1);
}
}
return cachedP ;
}
/**
* Return the effective numerical matrix rank.
* The effective numerical rank is the number of non-negligible
* singular values.
* This implementation looks at Frobenius norms of the sequence of
* bottom right submatrices. When a large fall in norm is seen,
* the rank is returned. The drop is computed as:
*
* (thisNorm/lastNorm) * rNorm < dropThreshold
*
*
* where thisNorm is the Frobenius norm of the current submatrix,
* lastNorm is the Frobenius norm of the previous submatrix,
* rNorm is is the Frobenius norm of the complete matrix
*
*
* @param dropThreshold threshold triggering rank computation
* @return effective numerical matrix rank
*/
public int getRank(final double dropThreshold) {
RealMatrix r = getR();
int rows = r.getRowDimension();
int columns = r.getColumnDimension();
int rank = 1;
double lastNorm = r.getFrobeniusNorm();
double rNorm = lastNorm;
while (rank < FastMath.min(rows, columns)) {
double thisNorm = r.getSubMatrix(rank, rows - 1, rank, columns - 1).getFrobeniusNorm();
if (thisNorm == 0 || (thisNorm / lastNorm) * rNorm < dropThreshold) {
break;
}
lastNorm = thisNorm;
rank++;
}
return rank;
}
/**
* Get a solver for finding the A × X = B solution in least square sense.
*
* 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 RRQRDecomposition#RRQRDecomposition(RealMatrix,
* double) construction}, an error will be triggered when
* the {@link DecompositionSolver#solve(RealVector) solve} method will be called.
*
* @return a solver
*/
@Override
public DecompositionSolver getSolver() {
return new Solver(super.getSolver(), this.getP());
}
/** Specialized solver. */
private static class Solver implements DecompositionSolver {
/** Upper level solver. */
private final DecompositionSolver upper;
/** A permutation matrix for the pivots used in the QR decomposition */
private RealMatrix p;
/**
* Build a solver from decomposed matrix.
*
* @param upper upper level solver.
* @param p permutation matrix
*/
private Solver(final DecompositionSolver upper, final RealMatrix p) {
this.upper = upper;
this.p = p;
}
/** {@inheritDoc} */
public boolean isNonSingular() {
return upper.isNonSingular();
}
/** {@inheritDoc} */
public RealVector solve(RealVector b) {
return p.operate(upper.solve(b));
}
/** {@inheritDoc} */
public RealMatrix solve(RealMatrix b) {
return p.multiply(upper.solve(b));
}
/**
* {@inheritDoc}
* @throws SingularMatrixException if the decomposed matrix is singular.
*/
public RealMatrix getInverse() {
return solve(MatrixUtils.createRealIdentityMatrix(p.getRowDimension()));
}
}
}