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/*
* 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.univariate;
import org.apache.commons.math3.util.Incrementor;
import org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.exception.TooManyEvaluationsException;
import org.apache.commons.math3.exception.NullArgumentException;
import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.ConvergenceChecker;
/**
* Provide a default implementation for several functions useful to generic
* optimizers.
*
* @deprecated As of 3.1 (to be removed in 4.0).
* @since 2.0
*/
@Deprecated
public abstract class BaseAbstractUnivariateOptimizer
implements UnivariateOptimizer {
/** Convergence checker. */
private final ConvergenceChecker<UnivariatePointValuePair> checker;
/** Evaluations counter. */
private final Incrementor evaluations = new Incrementor();
/** Optimization type */
private GoalType goal;
/** Lower end of search interval. */
private double searchMin;
/** Higher end of search interval. */
private double searchMax;
/** Initial guess . */
private double searchStart;
/** Function to optimize. */
private UnivariateFunction function;
/**
* @param checker Convergence checking procedure.
*/
protected BaseAbstractUnivariateOptimizer(ConvergenceChecker<UnivariatePointValuePair> checker) {
this.checker = checker;
}
/** {@inheritDoc} */
public int getMaxEvaluations() {
return evaluations.getMaximalCount();
}
/** {@inheritDoc} */
public int getEvaluations() {
return evaluations.getCount();
}
/**
* @return the optimization type.
*/
public GoalType getGoalType() {
return goal;
}
/**
* @return the lower end of the search interval.
*/
public double getMin() {
return searchMin;
}
/**
* @return the higher end of the search interval.
*/
public double getMax() {
return searchMax;
}
/**
* @return the initial guess.
*/
public double getStartValue() {
return searchStart;
}
/**
* Compute the objective function value.
*
* @param point Point at which the objective function must be evaluated.
* @return the objective function value at specified point.
* @throws TooManyEvaluationsException if the maximal number of evaluations
* is exceeded.
*/
protected double computeObjectiveValue(double point) {
try {
evaluations.incrementCount();
} catch (MaxCountExceededException e) {
throw new TooManyEvaluationsException(e.getMax());
}
return function.value(point);
}
/** {@inheritDoc} */
public UnivariatePointValuePair optimize(int maxEval, UnivariateFunction f,
GoalType goalType,
double min, double max,
double startValue) {
// Checks.
if (f == null) {
throw new NullArgumentException();
}
if (goalType == null) {
throw new NullArgumentException();
}
// Reset.
searchMin = min;
searchMax = max;
searchStart = startValue;
goal = goalType;
function = f;
evaluations.setMaximalCount(maxEval);
evaluations.resetCount();
// Perform computation.
return doOptimize();
}
/** {@inheritDoc} */
public UnivariatePointValuePair optimize(int maxEval,
UnivariateFunction f,
GoalType goalType,
double min, double max){
return optimize(maxEval, f, goalType, min, max, min + 0.5 * (max - min));
}
/**
* {@inheritDoc}
*/
public ConvergenceChecker<UnivariatePointValuePair> getConvergenceChecker() {
return checker;
}
/**
* Method for implementing actual optimization algorithms in derived
* classes.
*
* @return the optimum and its corresponding function value.
* @throws TooManyEvaluationsException if the maximal number of evaluations
* is exceeded.
*/
protected abstract UnivariatePointValuePair doOptimize();
}
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