/*
 * Copyright (c) 2009-2020, 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;

/**
 * <p>
 * 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.
 * </p>
 *
 * @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 < end; iEl++, jEl++) {
//                    sum -= el[i*n+k]*el[j*n+k];
                    sum -= t[iEl]*t[jEl];
                }

                if (i == j) {
                    // is it positive-definite?
                    if (sum <= 0.0)
                        return false;

                    el_ii = Math.sqrt(sum);
                    t[i*n + i] = el_ii;
                    div_el_ii = 1.0/el_ii;
                } else {
                    t[j*n + i] = sum*div_el_ii;
                }
            }
        }

        // zero the top right corner.
        for (int i = 0; i < n; i++) {
            for (int j = i + 1; j < n; j++) {
                t[i*n + j] = 0.0;
            }
        }

        return true;
    }

    @Override
    protected boolean decomposeUpper() {
        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];

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

                if (i == j) {
                    // is it positive-definite?
                    if (sum <= 0.0)
                        return false;

                    // I suspect that the sqrt is slowing this down relative to MTJ
                    el_ii = Math.sqrt(sum);
                    t[i*n + i] = el_ii;
                    div_el_ii = 1.0/el_ii;
                } else {
                    t[i*n + j] = sum*div_el_ii;
                }
            }
        }
        // zero the lower left corner.
        for (int i = 0; i < n; i++) {
            for (int j = 0; j < i; j++) {
                t[i*n + j] = 0.0;
            }
        }

        return true;
    }
}
