    001    /*
    002     * Licensed to the Apache Software Foundation (ASF) under one or more
    003     * contributor license agreements.  See the NOTICE file distributed with
    004     * this work for additional information regarding copyright ownership.
    005     * The ASF licenses this file to You under the Apache License, Version 2.0
    006     * (the "License"); you may not use this file except in compliance with
    007     * the License.  You may obtain a copy of the License at
    008     *
    009     *      http://www.apache.org/licenses/LICENSE-2.0
    010     *
    011     * Unless required by applicable law or agreed to in writing, software
    012     * distributed under the License is distributed on an "AS IS" BASIS,
    013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    014     * See the License for the specific language governing permissions and
    015     * limitations under the License.
    016     */
    017    package org.apache.commons.math3.optim.nonlinear.scalar;
    018
    019    import org.apache.commons.math3.analysis.MultivariateVectorFunction;
    020    import org.apache.commons.math3.optim.ConvergenceChecker;
    021    import org.apache.commons.math3.optim.OptimizationData;
    022    import org.apache.commons.math3.optim.PointValuePair;
    023    import org.apache.commons.math3.exception.TooManyEvaluationsException;
    024
    025    /**
    026     * Base class for implementing optimizers for multivariate scalar
    027     * differentiable functions.
    028     * It contains boiler-plate code for dealing with gradient evaluation.
    029     *
    030     * @version $Id$
    031     * @since 3.1
    032     */
    033    public abstract class GradientMultivariateOptimizer
    034        extends MultivariateOptimizer {
    035        /**
    036         * Gradient of the objective function.
    037         */
    038        private MultivariateVectorFunction gradient;
    039
    040        /**
    041         * @param checker Convergence checker.
    042         */
    043        protected GradientMultivariateOptimizer(ConvergenceChecker<PointValuePair> checker) {
    044            super(checker);
    045        }
    046
    047        /**
    048         * Compute the gradient vector.
    049         *
    050         * @param params Point at which the gradient must be evaluated.
    051         * @return the gradient at the specified point.
    052         */
    053        protected double[] computeObjectiveGradient(final double[] params) {
    054            return gradient.value(params);
    055        }
    056
    057        /**
    058         * {@inheritDoc}
    059         *
    060         * @param optData Optimization data.
    061         * The following data will be looked for:
    062         * <ul>
    063         *  <li>{@link org.apache.commons.math3.optim.MaxEval}</li>
    064         *  <li>{@link org.apache.commons.math3.optim.InitialGuess}</li>
    065         *  <li>{@link org.apache.commons.math3.optim.SimpleBounds}</li>
    066         *  <li>{@link ObjectiveFunction}</li>
    067         *  <li>{@link GoalType}</li>
    068         *  <li>{@link ObjectiveFunctionGradient}</li>
    069         * </ul>
    070         * @return {@inheritDoc}
    071         * @throws TooManyEvaluationsException if the maximal number of
    072         * evaluations (of the objective function) is exceeded.
    073         */
    074        @Override
    075        public PointValuePair optimize(OptimizationData... optData)
    076            throws TooManyEvaluationsException {
    077             // Retrieve settings.
    078            parseOptimizationData(optData);
    079            // Set up base class and perform computation.
    080            return super.optimize(optData);
    081        }
    082
    083        /**
    084         * Scans the list of (required and optional) optimization data that
    085         * characterize the problem.
    086         *
    087         * @param optData Optimization data.
    088         * The following data will be looked for:
    089         * <ul>
    090         *  <li>{@link ObjectiveFunctionGradient}</li>
    091         * </ul>
    092         */
    093        private void parseOptimizationData(OptimizationData... optData) {
    094            // The existing values (as set by the previous call) are reused if
    095            // not provided in the argument list.
    096            for (OptimizationData data : optData) {
    097                if  (data instanceof ObjectiveFunctionGradient) {
    098                    gradient = ((ObjectiveFunctionGradient) data).getObjectiveFunctionGradient();
    099                    // If more data must be parsed, this statement _must_ be
    100                    // changed to "continue".
    101                    break;
    102                }
    103            }
    104        }
    105    }