AbstractDifferentiableOptimizer.java
- /*
- * 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);
- }
- }