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: GradientMultivariateOptimizer.java 1443444 2013-02-07 12:41:36Z erans $
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. In addition to those documented in
061         * {@link MultivariateOptimizer#parseOptimizationData(OptimizationData[])
062         * MultivariateOptimizer}, this method will register the following data:
063         * <ul>
064         *  <li>{@link ObjectiveFunctionGradient}</li>
065         * </ul>
066         * @return {@inheritDoc}
067         * @throws TooManyEvaluationsException if the maximal number of
068         * evaluations (of the objective function) is exceeded.
069         */
070        @Override
071        public PointValuePair optimize(OptimizationData... optData)
072            throws TooManyEvaluationsException {
073            // Set up base class and perform computation.
074            return super.optimize(optData);
075        }
076    
077        /**
078         * Scans the list of (required and optional) optimization data that
079         * characterize the problem.
080         *
081         * @param optData Optimization data.
082         * The following data will be looked for:
083         * <ul>
084         *  <li>{@link ObjectiveFunctionGradient}</li>
085         * </ul>
086         */
087        @Override
088        protected void parseOptimizationData(OptimizationData... optData) {
089            // Allow base class to register its own data.
090            super.parseOptimizationData(optData);
091    
092            // The existing values (as set by the previous call) are reused if
093            // not provided in the argument list.
094            for (OptimizationData data : optData) {
095                if  (data instanceof ObjectiveFunctionGradient) {
096                    gradient = ((ObjectiveFunctionGradient) data).getObjectiveFunctionGradient();
097                    // If more data must be parsed, this statement _must_ be
098                    // changed to "continue".
099                    break;
100                }
101            }
102        }
103    }