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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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package org.apache.commons.math.optimization;
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import static org.junit.Assert.assertEquals;
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import static org.junit.Assert.assertTrue;
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import org.apache.commons.math.ConvergenceException;
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import org.apache.commons.math.FunctionEvaluationException;
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import org.apache.commons.math.analysis.MultivariateRealFunction;
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import org.apache.commons.math.optimization.direct.NelderMead;
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import org.apache.commons.math.random.GaussianRandomGenerator;
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import org.apache.commons.math.random.JDKRandomGenerator;
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import org.apache.commons.math.random.RandomVectorGenerator;
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import org.apache.commons.math.random.UncorrelatedRandomVectorGenerator;
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import org.junit.Test;
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public class MultiStartMultivariateRealOptimizerTest {
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public void testRosenbrock()
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throws FunctionEvaluationException, ConvergenceException {
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Rosenbrock rosenbrock = new Rosenbrock();
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NelderMead underlying = new NelderMead();
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underlying.setStartConfiguration(new double[][] {
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{ -1.2, 1.0 }, { 0.9, 1.2 } , { 3.5, -2.3 }
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JDKRandomGenerator g = new JDKRandomGenerator();
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g.setSeed(16069223052l);
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RandomVectorGenerator generator =
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new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
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MultiStartMultivariateRealOptimizer optimizer =
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new MultiStartMultivariateRealOptimizer(underlying, 10, generator);
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optimizer.setConvergenceChecker(new SimpleScalarValueChecker(-1, 1.0e-3));
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optimizer.setMaxIterations(100);
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RealPointValuePair optimum =
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optimizer.optimize(rosenbrock, GoalType.MINIMIZE, new double[] { -1.2, 1.0 });
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assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
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assertTrue(optimizer.getEvaluations() > 20);
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assertTrue(optimizer.getEvaluations() < 250);
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assertTrue(optimum.getValue() < 8.0e-4);
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private static class Rosenbrock implements MultivariateRealFunction {
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public double value(double[] x) throws FunctionEvaluationException {
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double a = x[1] - x[0] * x[0];
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double b = 1.0 - x[0];
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return 100 * a * a + b * b;
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public int getCount() {