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 }