/*
* 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.optim.univariate;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.exception.NotStrictlyPositiveException;
import org.apache.commons.math3.optim.AbstractConvergenceChecker;
/**
* Simple implementation of the
* {@link org.apache.commons.math3.optimization.ConvergenceChecker} interface
* that uses only objective function values.
*
* Convergence is considered to have been reached if either the relative
* difference between the objective function values is smaller than a
* threshold or if either the absolute difference between the objective
* function values is smaller than another threshold.
* <br/>
* The {@link #converged(int,UnivariatePointValuePair,UnivariatePointValuePair)
* converged} method will also return {@code true} if the number of iterations
* has been set (see {@link #SimpleUnivariateValueChecker(double,double,int)
* this constructor}).
*
* @since 3.1
*/
public class SimpleUnivariateValueChecker
extends AbstractConvergenceChecker<UnivariatePointValuePair> {
/**
* If {@link #maxIterationCount} is set to this value, the number of
* iterations will never cause
* {@link #converged(int,UnivariatePointValuePair,UnivariatePointValuePair)}
* to return {@code true}.
*/
private static final int ITERATION_CHECK_DISABLED = -1;
/**
* Number of iterations after which the
* {@link #converged(int,UnivariatePointValuePair,UnivariatePointValuePair)}
* method will return true (unless the check is disabled).
*/
private final int maxIterationCount;
/** Build an instance with specified thresholds.
*
* In order to perform only relative checks, the absolute tolerance
* must be set to a negative value. In order to perform only absolute
* checks, the relative tolerance must be set to a negative value.
*
* @param relativeThreshold relative tolerance threshold
* @param absoluteThreshold absolute tolerance threshold
*/
public SimpleUnivariateValueChecker(final double relativeThreshold,
final double absoluteThreshold) {
super(relativeThreshold, absoluteThreshold);
maxIterationCount = ITERATION_CHECK_DISABLED;
}
/**
* Builds an instance with specified thresholds.
*
* In order to perform only relative checks, the absolute tolerance
* must be set to a negative value. In order to perform only absolute
* checks, the relative tolerance must be set to a negative value.
*
* @param relativeThreshold relative tolerance threshold
* @param absoluteThreshold absolute tolerance threshold
* @param maxIter Maximum iteration count.
* @throws NotStrictlyPositiveException if {@code maxIter <= 0}.
*
* @since 3.1
*/
public SimpleUnivariateValueChecker(final double relativeThreshold,
final double absoluteThreshold,
final int maxIter) {
super(relativeThreshold, absoluteThreshold);
if (maxIter <= 0) {
throw new NotStrictlyPositiveException(maxIter);
}
maxIterationCount = maxIter;
}
/**
* Check if the optimization algorithm has converged considering the
* last two points.
* This method may be called several time from the same algorithm
* iteration with different points. This can be detected by checking the
* iteration number at each call if needed. Each time this method is
* called, the previous and current point correspond to points with the
* same role at each iteration, so they can be compared. As an example,
* simplex-based algorithms call this method for all points of the simplex,
* not only for the best or worst ones.
*
* @param iteration Index of current iteration
* @param previous Best point in the previous iteration.
* @param current Best point in the current iteration.
* @return {@code true} if the algorithm has converged.
*/
@Override
public boolean converged(final int iteration,
final UnivariatePointValuePair previous,
final UnivariatePointValuePair current) {
if (maxIterationCount != ITERATION_CHECK_DISABLED && iteration >= maxIterationCount) {
return true;
}
final double p = previous.getValue();
final double c = current.getValue();
final double difference = FastMath.abs(p - c);
final double size = FastMath.max(FastMath.abs(p), FastMath.abs(c));
return difference <= size * getRelativeThreshold() ||
difference <= getAbsoluteThreshold();
}
}