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Rugged is an Orekit add-on handling Digital Elevation Models contribution to line of sight computation
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/* Copyright 2013-2019 CS Systèmes d'Information
 * Licensed to CS Systèmes d'Information (CS) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * CS licenses this file to You 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.orekit.rugged.adjustment;

import org.hipparchus.linear.LUDecomposer;
import org.hipparchus.linear.QRDecomposer;
import org.hipparchus.optim.nonlinear.vector.leastsquares.GaussNewtonOptimizer;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresOptimizer.Optimum;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem;
import org.hipparchus.optim.nonlinear.vector.leastsquares.LevenbergMarquardtOptimizer;
import org.orekit.rugged.errors.RuggedInternalError;

/** LeastSquareAdjuster
 * Class for setting least square algorithm chosen for solving optimization problem.
 * @author Jonathan Guinet
 * @author Lucie Labat-Allee
 * @author Guylaine Prat
 * @since 2.0
 */
public class LeastSquareAdjuster {

    /** Least square optimizer.*/
    private final LeastSquaresOptimizer adjuster;

    /** Least square optimizer choice.*/
    private final OptimizerId optimizerID;

    /** Constructor.
     * @param optimizerID optimizer choice
     */
    public LeastSquareAdjuster(final OptimizerId optimizerID) {

        this.optimizerID = optimizerID;
        this.adjuster = this.selectOptimizer();
    }

    /** Default constructor with Gauss Newton with QR decomposition algorithm.*/
    public LeastSquareAdjuster() {

        this.optimizerID = OptimizerId.GAUSS_NEWTON_QR;
        this.adjuster = this.selectOptimizer();
    }

    /** Solve the least square problem.
     * @param problem the least square problem
     * @return the solution
     */
    public Optimum optimize(final LeastSquaresProblem problem) {
        return this.adjuster.optimize(problem);
    }

    /** Create the optimizer.
     * @return the least square optimizer
     */
    private LeastSquaresOptimizer selectOptimizer() {

        // Set up the optimizer
        switch (this.optimizerID) {

            case LEVENBERG_MARQUADT:
                return new LevenbergMarquardtOptimizer();

            case GAUSS_NEWTON_LU :
                return new GaussNewtonOptimizer(new LUDecomposer(1e-11), true);

            case GAUSS_NEWTON_QR :
                return new GaussNewtonOptimizer(new QRDecomposer(1e-11), false);

            default :
                // this should never happen
                throw new RuggedInternalError(null);
        }
    }
}