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/*
 * Copyright (c) 2009-2017, 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 org.ejml.dense.row.decomposition.chol;

/**
 * 

* 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_DDRM} * but much slower on larger matrices. *

* * @author Peter Abeles */ public class CholeskyDecompositionInner_DDRM extends CholeskyDecompositionCommon_DDRM { public CholeskyDecompositionInner_DDRM() { super(true); } public CholeskyDecompositionInner_DDRM(boolean lower) { super(lower); } @Override protected boolean 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




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