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A fast and easy to use dense matrix linear algebra library written in Java.
/*
* Copyright (c) 2009-2012, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* EJML is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as
* published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* EJML is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with EJML. If not, see
* Finds the decomposition of a matrix in the form of:
*
* A = OHOT
*
* where A is an m by m matrix, O is an orthogonal matrix, and H is an upper Hessenberg matrix.
*
*
*
* A matrix is upper Hessenberg if aij = 0 for all i > j+1. For example, the following matrix
* is upper Hessenberg.
*
* WRITE IT OUT USING A TABLE
*
*
*
* This decomposition is primarily used as a step for computing the eigenvalue decomposition of a matrix.
* The basic algorithm comes from David S. Watkins, "Fundamentals of MatrixComputations" Second Edition.
*
*/
// TODO create a column based one similar to what was done for QR decomposition?
public class HessenbergSimilarDecomposition
implements DecompositionInterface {
// A combined matrix that stores te upper Hessenberg matrix and the orthogonal matrix.
private DenseMatrix64F QH;
// number of rows and columns of the matrix being decompose
private int N;
// the first element in the orthogonal vectors
private double gammas[];
// temporary storage
private double b[];
private double u[];
/**
* Creates a decomposition that won't need to allocate new memory if it is passed matrices up to
* the specified size.
*
* @param initialSize Expected size of the matrices it will decompose.
*/
public HessenbergSimilarDecomposition( int initialSize ) {
gammas = new double[ initialSize ];
b = new double[ initialSize ];
u = new double[ initialSize ];
}
public HessenbergSimilarDecomposition() {
this(5);
}
/**
* Computes the decomposition of the provided matrix. If no errors are detected then true is returned,
* false otherwise.
* @param A The matrix that is being decomposed. Not modified.
* @return If it detects any errors or not.
*/
@Override
public boolean decompose( DenseMatrix64F A )
{
if( A.numRows != A.numCols )
throw new IllegalArgumentException("A must be square.");
QH = A;
N = A.numCols;
if( b.length < N ) {
b = new double[ N ];
gammas = new double[ N ];
u = new double[ N ];
}
return _decompose();
}
@Override
public boolean inputModified() {
return true;
}
/**
* The raw QH matrix that is stored internally.
*
* @return QH matrix.
*/
public DenseMatrix64F getQH() {
return QH;
}
/**
* An upper Hessenberg matrix from the decompostion.
*
* @param H If not null then the results will be stored here. Otherwise a new matrix will be created.
* @return The extracted H matrix.
*/
public DenseMatrix64F getH( DenseMatrix64F H ) {
if( H == null ) {
H = new DenseMatrix64F(N,N);
} else if( N != H.numRows || N != H.numCols )
throw new IllegalArgumentException("The provided H must have the same dimensions as the decomposed matrix.");
else
H.zero();
// copy the first row
System.arraycopy(QH.data, 0, H.data, 0, N);
for( int i = 1; i < N; i++ ) {
for( int j = i-1; j < N; j++ ) {
H.set(i,j, QH.get(i,j));
}
}
return H;
}
/**
* An orthogonal matrix that has the following property: H = QTAQ
*
* @param Q If not null then the results will be stored here. Otherwise a new matrix will be created.
* @return The extracted Q matrix.
*/
public DenseMatrix64F getQ( DenseMatrix64F Q ) {
if( Q == null ) {
Q = new DenseMatrix64F(N,N);
for( int i = 0; i < N; i++ ) {
Q.data[i*N+i] = 1;
}
} else if( N != Q.numRows || N != Q.numCols )
throw new IllegalArgumentException("The provided H must have the same dimensions as the decomposed matrix.");
else
CommonOps.setIdentity(Q);
for( int j = N-2; j >= 0; j-- ) {
u[j+1] = 1;
for( int i = j+2; i < N; i++ ) {
u[i] = QH.get(i,j);
}
QrHelperFunctions.rank1UpdateMultR(Q,u,gammas[j],j+1,j+1,N,b);
}
return Q;
}
/**
* Internal function for computing the decomposition.
*/
private boolean _decompose() {
double h[] = QH.data;
for( int k = 0; k < N-2; k++ ) {
// find the largest value in this column
// this is used to normalize the column and mitigate overflow/underflow
double max = 0;
for( int i = k+1; i < N; i++ ) {
// copy the householder vector to vector outside of the matrix to reduce caching issues
// big improvement on larger matrices and a relatively small performance hit on small matrices.
double val = u[i] = h[i*N+k];
val = Math.abs(val);
if( val > max )
max = val;
}
if( max > 0 ) {
// -------- set up the reflector Q_k
double tau = 0;
// normalize to reduce overflow/underflow
// and compute tau for the reflector
for( int i = k+1; i < N; i++ ) {
double val = u[i] /= max;
tau += val*val;
}
tau = Math.sqrt(tau);
if( u[k+1] < 0 )
tau = -tau;
// write the reflector into the lower left column of the matrix
double nu = u[k+1] + tau;
u[k+1] = 1.0;
for( int i = k+2; i < N; i++ ) {
h[i*N+k] = u[i] /= nu;
}
double gamma = nu/tau;
gammas[k] = gamma;
// ---------- multiply on the left by Q_k
QrHelperFunctions.rank1UpdateMultR(QH,u,gamma,k+1,k+1,N,b);
// ---------- multiply on the right by Q_k
QrHelperFunctions.rank1UpdateMultL(QH,u,gamma,0,k+1,N);
// since the first element in the householder vector is known to be 1
// store the full upper hessenberg
h[(k+1)*N+k] = -tau*max;
} else {
gammas[k] = 0;
}
}
return true;
}
public double[] getGammas() {
return gammas;
}
}