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.DifferentiableMultivariateFunction;
021    import org.apache.commons.math3.analysis.MultivariateVectorFunction;
022    import org.apache.commons.math3.analysis.FunctionUtils;
023    import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction;
024    import org.apache.commons.math3.optimization.DifferentiableMultivariateOptimizer;
025    import org.apache.commons.math3.optimization.GoalType;
026    import org.apache.commons.math3.optimization.ConvergenceChecker;
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: AbstractScalarDifferentiableOptimizer.java 1422230 2012-12-15 12:11:13Z erans $
036     * @deprecated As of 3.1 (to be removed in 4.0).
037     * @since 2.0
038     */
039    @Deprecated
040    public abstract class AbstractScalarDifferentiableOptimizer
041        extends BaseAbstractMultivariateOptimizer<DifferentiableMultivariateFunction>
042        implements DifferentiableMultivariateOptimizer {
043        /**
044         * Objective function gradient.
045         */
046        private MultivariateVectorFunction gradient;
047    
048        /**
049         * Simple constructor with default settings.
050         * The convergence check is set to a
051         * {@link org.apache.commons.math3.optimization.SimpleValueChecker
052         * SimpleValueChecker}.
053         * @deprecated See {@link org.apache.commons.math3.optimization.SimpleValueChecker#SimpleValueChecker()}
054         */
055        @Deprecated
056        protected AbstractScalarDifferentiableOptimizer() {}
057    
058        /**
059         * @param checker Convergence checker.
060         */
061        protected AbstractScalarDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) {
062            super(checker);
063        }
064    
065        /**
066         * Compute the gradient vector.
067         *
068         * @param evaluationPoint Point at which the gradient must be evaluated.
069         * @return the gradient at the specified point.
070         * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
071         * if the allowed number of evaluations is exceeded.
072         */
073        protected double[] computeObjectiveGradient(final double[] evaluationPoint) {
074            return gradient.value(evaluationPoint);
075        }
076    
077        /** {@inheritDoc} */
078        @Override
079        protected PointValuePair optimizeInternal(int maxEval,
080                                                  final DifferentiableMultivariateFunction f,
081                                                  final GoalType goalType,
082                                                  final double[] startPoint) {
083            // Store optimization problem characteristics.
084            gradient = f.gradient();
085    
086            return super.optimizeInternal(maxEval, f, goalType, startPoint);
087        }
088    
089        /**
090         * Optimize an objective function.
091         *
092         * @param f Objective function.
093         * @param goalType Type of optimization goal: either
094         * {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
095         * @param startPoint Start point for optimization.
096         * @param maxEval Maximum number of function evaluations.
097         * @return the point/value pair giving the optimal value for objective
098         * function.
099         * @throws org.apache.commons.math3.exception.DimensionMismatchException
100         * if the start point dimension is wrong.
101         * @throws org.apache.commons.math3.exception.TooManyEvaluationsException
102         * if the maximal number of evaluations is exceeded.
103         * @throws org.apache.commons.math3.exception.NullArgumentException if
104         * any argument is {@code null}.
105         */
106        public PointValuePair optimize(final int maxEval,
107                                       final MultivariateDifferentiableFunction f,
108                                       final GoalType goalType,
109                                       final double[] startPoint) {
110            return optimizeInternal(maxEval,
111                                    FunctionUtils.toDifferentiableMultivariateFunction(f),
112                                    goalType,
113                                    startPoint);
114        }
115    }