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With inspiration from other libraries
/*
* 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.direct;
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.analysis.MultivariateFunction;
import org.apache.commons.math3.optimization.BaseMultivariateOptimizer;
import org.apache.commons.math3.optimization.OptimizationData;
import org.apache.commons.math3.optimization.GoalType;
import org.apache.commons.math3.optimization.InitialGuess;
import org.apache.commons.math3.optimization.SimpleBounds;
import org.apache.commons.math3.optimization.ConvergenceChecker;
import org.apache.commons.math3.optimization.PointValuePair;
import org.apache.commons.math3.optimization.SimpleValueChecker;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.NumberIsTooLargeException;
/**
* Base class for implementing optimizers for multivariate scalar functions.
* This base class handles the boiler-plate methods associated to thresholds,
* evaluations counting, initial guess and simple bounds settings.
*
* @param Type of the objective function to be optimized.
*
* @deprecated As of 3.1 (to be removed in 4.0).
* @since 2.2
*/
@Deprecated
public abstract class BaseAbstractMultivariateOptimizer
implements BaseMultivariateOptimizer {
/** Evaluations counter. */
protected final Incrementor evaluations = new Incrementor();
/** Convergence checker. */
private ConvergenceChecker checker;
/** Type of optimization. */
private GoalType goal;
/** Initial guess. */
private double[] start;
/** Lower bounds. */
private double[] lowerBound;
/** Upper bounds. */
private double[] upperBound;
/** Objective function. */
private MultivariateFunction function;
/**
* Simple constructor with default settings.
* The convergence check is set to a {@link SimpleValueChecker}.
* @deprecated See {@link SimpleValueChecker#SimpleValueChecker()}
*/
@Deprecated
protected BaseAbstractMultivariateOptimizer() {
this(new SimpleValueChecker());
}
/**
* @param checker Convergence checker.
*/
protected BaseAbstractMultivariateOptimizer(ConvergenceChecker checker) {
this.checker = checker;
}
/** {@inheritDoc} */
public int getMaxEvaluations() {
return evaluations.getMaximalCount();
}
/** {@inheritDoc} */
public int getEvaluations() {
return evaluations.getCount();
}
/** {@inheritDoc} */
public ConvergenceChecker getConvergenceChecker() {
return checker;
}
/**
* Compute the objective function value.
*
* @param point Point at which the objective function must be evaluated.
* @return the objective function value at the 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}
*
* @deprecated As of 3.1. Please use
* {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])}
* instead.
*/
@Deprecated
public PointValuePair optimize(int maxEval, FUNC f, GoalType goalType,
double[] startPoint) {
return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
}
/**
* Optimize an objective function.
*
* @param maxEval Allowed number of evaluations of the objective function.
* @param f Objective function.
* @param goalType Optimization type.
* @param optData Optimization data. The following data will be looked for:
*
* - {@link InitialGuess}
* - {@link SimpleBounds}
*
* @return the point/value pair giving the optimal value of the objective
* function.
* @since 3.1
*/
public PointValuePair optimize(int maxEval,
FUNC f,
GoalType goalType,
OptimizationData... optData) {
return optimizeInternal(maxEval, f, goalType, optData);
}
/**
* Optimize an objective function.
*
* @param f Objective function.
* @param goalType Type of optimization goal: either
* {@link GoalType#MAXIMIZE} or {@link GoalType#MINIMIZE}.
* @param startPoint Start point for optimization.
* @param maxEval Maximum number of function evaluations.
* @return the point/value pair giving the optimal value for objective
* function.
* @throws org.apache.commons.math3.exception.DimensionMismatchException
* if the start point dimension is wrong.
* @throws org.apache.commons.math3.exception.TooManyEvaluationsException
* if the maximal number of evaluations is exceeded.
* @throws org.apache.commons.math3.exception.NullArgumentException if
* any argument is {@code null}.
* @deprecated As of 3.1. Please use
* {@link #optimize(int,MultivariateFunction,GoalType,OptimizationData[])}
* instead.
*/
@Deprecated
protected PointValuePair optimizeInternal(int maxEval, FUNC f, GoalType goalType,
double[] startPoint) {
return optimizeInternal(maxEval, f, goalType, new InitialGuess(startPoint));
}
/**
* Optimize an objective function.
*
* @param maxEval Allowed number of evaluations of the objective function.
* @param f Objective function.
* @param goalType Optimization type.
* @param optData Optimization data. The following data will be looked for:
*
* - {@link InitialGuess}
* - {@link SimpleBounds}
*
* @return the point/value pair giving the optimal value of the objective
* function.
* @throws TooManyEvaluationsException if the maximal number of
* evaluations is exceeded.
* @since 3.1
*/
protected PointValuePair optimizeInternal(int maxEval,
FUNC f,
GoalType goalType,
OptimizationData... optData)
throws TooManyEvaluationsException {
// Set internal state.
evaluations.setMaximalCount(maxEval);
evaluations.resetCount();
function = f;
goal = goalType;
// Retrieve other settings.
parseOptimizationData(optData);
// Check input consistency.
checkParameters();
// Perform computation.
return doOptimize();
}
/**
* Scans the list of (required and optional) optimization data that
* characterize the problem.
*
* @param optData Optimization data. The following data will be looked for:
*
* - {@link InitialGuess}
* - {@link SimpleBounds}
*
*/
private void parseOptimizationData(OptimizationData... optData) {
// The existing values (as set by the previous call) are reused if
// not provided in the argument list.
for (OptimizationData data : optData) {
if (data instanceof InitialGuess) {
start = ((InitialGuess) data).getInitialGuess();
continue;
}
if (data instanceof SimpleBounds) {
final SimpleBounds bounds = (SimpleBounds) data;
lowerBound = bounds.getLower();
upperBound = bounds.getUpper();
continue;
}
}
}
/**
* @return the optimization type.
*/
public GoalType getGoalType() {
return goal;
}
/**
* @return the initial guess.
*/
public double[] getStartPoint() {
return start == null ? null : start.clone();
}
/**
* @return the lower bounds.
* @since 3.1
*/
public double[] getLowerBound() {
return lowerBound == null ? null : lowerBound.clone();
}
/**
* @return the upper bounds.
* @since 3.1
*/
public double[] getUpperBound() {
return upperBound == null ? null : upperBound.clone();
}
/**
* Perform the bulk of the optimization algorithm.
*
* @return the point/value pair giving the optimal value of the
* objective function.
*/
protected abstract PointValuePair doOptimize();
/**
* Check parameters consistency.
*/
private void checkParameters() {
if (start != null) {
final int dim = start.length;
if (lowerBound != null) {
if (lowerBound.length != dim) {
throw new DimensionMismatchException(lowerBound.length, dim);
}
for (int i = 0; i < dim; i++) {
final double v = start[i];
final double lo = lowerBound[i];
if (v < lo) {
throw new NumberIsTooSmallException(v, lo, true);
}
}
}
if (upperBound != null) {
if (upperBound.length != dim) {
throw new DimensionMismatchException(upperBound.length, dim);
}
for (int i = 0; i < dim; i++) {
final double v = start[i];
final double hi = upperBound[i];
if (v > hi) {
throw new NumberIsTooLargeException(v, hi, true);
}
}
}
// If the bounds were not specified, the allowed interval is
// assumed to be [-inf, +inf].
if (lowerBound == null) {
lowerBound = new double[dim];
for (int i = 0; i < dim; i++) {
lowerBound[i] = Double.NEGATIVE_INFINITY;
}
}
if (upperBound == null) {
upperBound = new double[dim];
for (int i = 0; i < dim; i++) {
upperBound[i] = Double.POSITIVE_INFINITY;
}
}
}
}
}