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Fast double-precision vector and matrix maths library for Java, supporting N-dimensional numeric arrays.

There is a newer version: 0.67.0

/*
 * 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.decompose.ICholeskyResult;

/**
 * 
 * This implementation of a Cholesky decomposition using the inner-product form.
 * For large matrices a block implementation is better.  On larger matrices the lower triangular
 * decomposition is significantly faster.  This is faster on smaller matrices than {@link CholeskyDecompositionBlock}
 * but much slower on larger matrices.
 * 
 *
 * @author Peter Abeles
 */
public class CholeskyInner extends CholeskyCommon {

	/**
     * 
     * Computes the Cholesky LDU Decomposition (A = LDU) of a 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.
     */
	public static ICholeskyResult decompose(AMatrix mat) {
		CholeskyInner temp = new CholeskyInner();
		return temp._decompose(mat);
	}
	
    @Override
    protected CholeskyResult decomposeLower() {
        double el_ii;
        double div_el_ii=0;

        for( int i = 0; i < n; i++ ) {
            for( int j = i; j < n; j++ ) {
                double sum = t[i*n+j];

                int iEl = i*n;
                int jEl = j*n;
                int end = iEl+i;
                // k = 0:i-1
                for( ; iEl