| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You under the Apache License, Version 2.0 |
| * (the "License"); you may not use this file except in compliance with |
| * the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| package org.apache.commons.math3.optimization.general; |
| |
| import org.apache.commons.math3.analysis.MultivariateVectorFunction; |
| import org.apache.commons.math3.analysis.differentiation.GradientFunction; |
| import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction; |
| import org.apache.commons.math3.optimization.ConvergenceChecker; |
| import org.apache.commons.math3.optimization.GoalType; |
| import org.apache.commons.math3.optimization.OptimizationData; |
| import org.apache.commons.math3.optimization.InitialGuess; |
| import org.apache.commons.math3.optimization.PointValuePair; |
| import org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer; |
| |
| /** |
| * Base class for implementing optimizers for multivariate scalar |
| * differentiable functions. |
| * It contains boiler-plate code for dealing with gradient evaluation. |
| * |
| * @deprecated As of 3.1 (to be removed in 4.0). |
| * @since 3.1 |
| */ |
| @Deprecated |
| public abstract class AbstractDifferentiableOptimizer |
| extends BaseAbstractMultivariateOptimizer<MultivariateDifferentiableFunction> { |
| /** |
| * Objective function gradient. |
| */ |
| private MultivariateVectorFunction gradient; |
| |
| /** |
| * @param checker Convergence checker. |
| */ |
| protected AbstractDifferentiableOptimizer(ConvergenceChecker<PointValuePair> checker) { |
| super(checker); |
| } |
| |
| /** |
| * Compute the gradient vector. |
| * |
| * @param evaluationPoint Point at which the gradient must be evaluated. |
| * @return the gradient at the specified point. |
| */ |
| protected double[] computeObjectiveGradient(final double[] evaluationPoint) { |
| return gradient.value(evaluationPoint); |
| } |
| |
| /** |
| * {@inheritDoc} |
| * |
| * @deprecated In 3.1. Please use |
| * {@link #optimizeInternal(int,MultivariateDifferentiableFunction,GoalType,OptimizationData[])} |
| * instead. |
| */ |
| @Override@Deprecated |
| protected PointValuePair optimizeInternal(final int maxEval, |
| final MultivariateDifferentiableFunction f, |
| final GoalType goalType, |
| final double[] startPoint) { |
| return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint)); |
| } |
| |
| /** {@inheritDoc} */ |
| @Override |
| protected PointValuePair optimizeInternal(final int maxEval, |
| final MultivariateDifferentiableFunction f, |
| final GoalType goalType, |
| final OptimizationData... optData) { |
| // Store optimization problem characteristics. |
| gradient = new GradientFunction(f); |
| |
| // Perform optimization. |
| return super.optimizeInternal(maxEval, f, goalType, optData); |
| } |
| } |