/*
* 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 <http://www.gnu.org/licenses/>.
*/
package org.ejml.alg.dense.decomposition.eig.symm;
import org.ejml.alg.dense.decomposition.eig.SymmetricQRAlgorithmDecomposition;
import org.ejml.alg.dense.decomposition.hessenberg.TridiagonalDecompositionBlock;
import org.ejml.alg.dense.decomposition.hessenberg.TridiagonalDecompositionHouseholder;
import org.ejml.alg.dense.decomposition.hessenberg.TridiagonalSimilarDecomposition;
import org.ejml.data.DenseMatrix64F;
import org.ejml.factory.DecompositionFactory;
import org.ejml.factory.EigenDecomposition;
import org.ejml.ops.RandomMatrices;
import java.util.Random;
/**
* @author Peter Abeles
*/
public class BenchmarkSymmetricEigenDecomposition {
public static long symmTogether( DenseMatrix64F orig , int numTrials ) {
long prev = System.currentTimeMillis();
TridiagonalSimilarDecomposition<DenseMatrix64F> decomp = DecompositionFactory.tridiagonal(orig.numRows);
SymmetricQRAlgorithmDecomposition alg = new SymmetricQRAlgorithmDecomposition(decomp,true);
alg.setComputeVectorsWithValues(true);
for( long i = 0; i < numTrials; i++ ) {
if( !DecompositionFactory.decomposeSafe(alg,orig) ) {
throw new RuntimeException("Bad matrix");
}
}
return System.currentTimeMillis() - prev;
}
public static long symmSeparate( DenseMatrix64F orig , int numTrials ) {
long prev = System.currentTimeMillis();
TridiagonalSimilarDecomposition<DenseMatrix64F> decomp = DecompositionFactory.tridiagonal(orig.numRows);
SymmetricQRAlgorithmDecomposition alg = new SymmetricQRAlgorithmDecomposition(decomp,true);
alg.setComputeVectorsWithValues(false);
for( long i = 0; i < numTrials; i++ ) {
if( !DecompositionFactory.decomposeSafe(alg,orig) ) {
throw new RuntimeException("Bad matrix");
}
}
return System.currentTimeMillis() - prev;
}
public static long standardTridiag( DenseMatrix64F orig , int numTrials ) {
TridiagonalSimilarDecomposition<DenseMatrix64F> decomp = new TridiagonalDecompositionHouseholder();
SymmetricQRAlgorithmDecomposition alg = new SymmetricQRAlgorithmDecomposition(decomp,true);
long prev = System.currentTimeMillis();
for( long i = 0; i < numTrials; i++ ) {
if( !DecompositionFactory.decomposeSafe(alg,orig) ) {
throw new RuntimeException("Bad matrix");
}
}
return System.currentTimeMillis() - prev;
}
public static long blockTridiag( DenseMatrix64F orig , int numTrials ) {
TridiagonalSimilarDecomposition<DenseMatrix64F> decomp = new TridiagonalDecompositionBlock();
SymmetricQRAlgorithmDecomposition alg = new SymmetricQRAlgorithmDecomposition(decomp,true);
long prev = System.currentTimeMillis();
for( long i = 0; i < numTrials; i++ ) {
if( !DecompositionFactory.decomposeSafe(alg,orig) ) {
throw new RuntimeException("Bad matrix");
}
}
return System.currentTimeMillis() - prev;
}
public static long defaultSymm( DenseMatrix64F orig , int numTrials ) {
EigenDecomposition<DenseMatrix64F> alg = DecompositionFactory.eig(orig.numCols, true, true);
long prev = System.currentTimeMillis();
for( long i = 0; i < numTrials; i++ ) {
if( !DecompositionFactory.decomposeSafe(alg,orig) ) {
throw new RuntimeException("Bad matrix");
}
}
return System.currentTimeMillis() - prev;
}
private static void runAlgorithms( DenseMatrix64F mat , int numTrials )
{
// System.out.println("Together = "+ symmTogether(mat,numTrials));
// System.out.println("Separate = "+ symmSeparate(mat,numTrials));
System.out.println("Standard = "+ standardTridiag(mat,numTrials));
System.out.println("Block = "+ blockTridiag(mat,numTrials));
System.out.println("Default = "+ defaultSymm(mat,numTrials));
}
public static void main( String args [] ) {
Random rand = new Random(232423);
int size[] = new int[]{2,4,10,100,200,500,1000,2000,5000};
int trials[] = new int[]{2000000,400000,80000,300,40,4,1,1,1};
for( int i = 0; i < size.length; i++ ) {
int w = size[i];
System.out.printf("Decomposing size %3d for %12d trials\n",w,trials[i]);
DenseMatrix64F symMat = RandomMatrices.createSymmetric(w,-1,1,rand);
runAlgorithms(symMat,trials[i]);
}
}
}