    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
    018    package org.apache.commons.math3.optimization.general;
    019
    020    import org.apache.commons.math3.analysis.MultivariateVectorFunction;
    021    import org.apache.commons.math3.analysis.differentiation.GradientFunction;
    022    import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
    023    import org.apache.commons.math3.optimization.ConvergenceChecker;
    024    import org.apache.commons.math3.optimization.GoalType;
    025    import org.apache.commons.math3.optimization.OptimizationData;
    026    import org.apache.commons.math3.optimization.InitialGuess;
    027    import org.apache.commons.math3.optimization.PointValuePair;
    028    import org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer;
    029
    030    /**
    031     * Base class for implementing optimizers for multivariate scalar
    032     * differentiable functions.
    033     * It contains boiler-plate code for dealing with gradient evaluation.
    034     *
    035     * @version $Id: AbstractDifferentiableOptimizer.java 1422230 2012-12-15 12:11:13Z erans $
    036     * @deprecated As of 3.1 (to be removed in 4.0).
    037     * @since 3.1
    038     */
    039    @Deprecated
    040    public abstract class AbstractDifferentiableOptimizer
    041        extends BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction> {
    042        /**
    043         * Objective function gradient.
    044         */
    045        private MultivariateVectorFunction gradient;
    046
    047        /**
    048         * @param checker Convergence checker.
    049         */
    050        protected AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) {
    051            super(checker);
    052        }
    053
    054        /**
    055         * Compute the gradient vector.
    056         *
    057         * @param evaluationPoint Point at which the gradient must be evaluated.
    058         * @return the gradient at the specified point.
    059         */
    060        protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
    061            return gradient.value(evaluationPoint);
    062        }
    063
    064        /**
    065         * {@inheritDoc}
    066         *
    067         * @deprecated In 3.1. Please use
    068         * {@link #optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[])}
    069         * instead.
    070         */
    071        @Override@Deprecated
    072        protected PointValuePair optimizeInternal(final int maxEval,
    073                                                  final MultivariateDifferentiableFunction f,
    074                                                  final GoalType goalType,
    075                                                  final double[] startPoint) {
    076            return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
    077        }
    078
    079        /** {@inheritDoc} */
    080        @Override
    081        protected PointValuePair optimizeInternal(final int maxEval,
    082                                                  final MultivariateDifferentiableFunction f,
    083                                                  final GoalType goalType,
    084                                                  final OptimizationData... optData) {
    085            // Store optimization problem characteristics.
    086            gradient = new GradientFunction(f);
    087
    088            // Perform optimization.
    089            return super.optimizeInternal(maxEval, f, goalType, optData);
    090        }
    091    }