mikera.matrixx.decompose.impl.chol.CholeskyCommon Maven / Gradle / Ivy
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/*
* Copyright (c) 2009-2013, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* Licensed 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 mikera.matrixx.decompose.impl.chol;
import mikera.matrixx.AMatrix;
import mikera.matrixx.Matrix;
import mikera.matrixx.decompose.ICholeskyResult;
/**
*
*
* This is an abstract class for a Cholesky decomposition. It provides the solvers, but the actual
* decompsoition is provided in other classes.
*
*
* A Cholesky Decomposition is a special decomposition for positive-definite symmetric matrices
* that is more efficient than other general purposes decomposition. It refactors matrices
* using one of the two following equations:
*
* L*LT=A
* RT*R=A
*
* where L is a lower triangular matrix and R is an upper traingular matrix.
*
*
* @see CholeskyDecompositionInner
* @see org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionBlock
* @see org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionLDL
*
* @author Peter Abeles
*/
public abstract class CholeskyCommon {
// width and height of the matrix
protected int n;
// the decomposed matrix
protected Matrix T;
protected double[] t;
// temporary variable used by various functions
protected double vv[];
/**
* Creates a CholeksyDecomposition capable of decomposing a matrix that is
* n by n, where n is the width.
*/
protected CholeskyCommon() {
}
/**
*
* Performs Choleksy decomposition on the provided matrix.
*
*
*
* If the matrix is not positive definite then this function will return
* null since it can't complete its computations. Not all errors will be
* found. This is an efficient way to check for positive definiteness.
*
* @param mat A symmetric positive definite matrix.
* @return ICholeskyResult if decomposition is successful, null otherwise.
*/
protected ICholeskyResult _decompose( AMatrix mat ) {
if( mat.rowCount() != mat.columnCount() ) {
throw new IllegalArgumentException("Must be a square matrix.");
}
n = mat.rowCount();
this.vv = new double[n];
T = mat.toMatrix();
t = T.data;
return decomposeLower();
}
/**
* Performs an lower triangular decomposition.
*/
protected abstract CholeskyResult decomposeLower();
}