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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.

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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.optim.univariate;

import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.optim.BaseOptimizer;
import org.apache.commons.math3.optim.OptimizationData;
import org.apache.commons.math3.optim.nonlinear.scalar.GoalType;
import org.apache.commons.math3.optim.ConvergenceChecker;
import org.apache.commons.math3.exception.TooManyEvaluationsException;

/**
 * Base class for a univariate scalar function optimizer.
 *
 * @since 3.1
 */
public abstract class UnivariateOptimizer
    extends BaseOptimizer {
    /** Objective function. */
    private UnivariateFunction function;
    /** Type of optimization. */
    private GoalType goal;
    /** Initial guess. */
    private double start;
    /** Lower bound. */
    private double min;
    /** Upper bound. */
    private double max;

    /**
     * @param checker Convergence checker.
     */
    protected UnivariateOptimizer(ConvergenceChecker checker) {
        super(checker);
    }

    /**
     * {@inheritDoc}
     *
     * @param optData Optimization data. In addition to those documented in
     * {@link BaseOptimizer#parseOptimizationData(OptimizationData[])
     * BaseOptimizer}, this method will register the following data:
     * 
    *
  • {@link GoalType}
  • *
  • {@link SearchInterval}
  • *
  • {@link UnivariateObjectiveFunction}
  • *
* @return {@inheritDoc} * @throws TooManyEvaluationsException if the maximal number of * evaluations is exceeded. */ @Override public UnivariatePointValuePair optimize(OptimizationData... optData) throws TooManyEvaluationsException { // Perform computation. return super.optimize(optData); } /** * @return the optimization type. */ public GoalType getGoalType() { return goal; } /** * 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 GoalType}
  • *
  • {@link SearchInterval}
  • *
  • {@link UnivariateObjectiveFunction}
  • *
*/ @Override protected void parseOptimizationData(OptimizationData... optData) { // Allow base class to register its own data. super.parseOptimizationData(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 SearchInterval) { final SearchInterval interval = (SearchInterval) data; min = interval.getMin(); max = interval.getMax(); start = interval.getStartValue(); continue; } if (data instanceof UnivariateObjectiveFunction) { function = ((UnivariateObjectiveFunction) data).getObjectiveFunction(); continue; } if (data instanceof GoalType) { goal = (GoalType) data; continue; } } } /** * @return the initial guess. */ public double getStartValue() { return start; } /** * @return the lower bounds. */ public double getMin() { return min; } /** * @return the upper bounds. */ public double getMax() { return max; } /** * Computes the objective function value. * This method must be called by subclasses to enforce the * evaluation counter limit. * * @param x 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 x) { super.incrementEvaluationCount(); return function.value(x); } }




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