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Java example source code file (MultiStartMultivariateOptimizerTest.java)

This example Java source code file (MultiStartMultivariateOptimizerTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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Java - Java tags/keywords

circlescalar, gaussianrandomgenerator, initialguess, jdkrandomgenerator, maxeval, multistartmultivariateoptimizer, multistartmultivariateoptimizertest, neldermeadsimplex, pointvaluepair, rosenbrock, simplevaluechecker, simplexoptimizer, test, uncorrelatedrandomvectorgenerator

The MultiStartMultivariateOptimizerTest.java Java example source code

/*
 * 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.nonlinear.scalar;

import org.apache.commons.math3.analysis.MultivariateFunction;
import org.apache.commons.math3.geometry.euclidean.twod.Vector2D;
import org.apache.commons.math3.optim.InitialGuess;
import org.apache.commons.math3.optim.MaxEval;
import org.apache.commons.math3.optim.PointValuePair;
import org.apache.commons.math3.optim.SimpleValueChecker;
import org.apache.commons.math3.optim.nonlinear.scalar.gradient.CircleScalar;
import org.apache.commons.math3.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.NelderMeadSimplex;
import org.apache.commons.math3.optim.nonlinear.scalar.noderiv.SimplexOptimizer;
import org.apache.commons.math3.random.GaussianRandomGenerator;
import org.apache.commons.math3.random.JDKRandomGenerator;
import org.apache.commons.math3.random.RandomVectorGenerator;
import org.apache.commons.math3.random.UncorrelatedRandomVectorGenerator;
import org.junit.Assert;
import org.junit.Test;

public class MultiStartMultivariateOptimizerTest {
    @Test
    public void testCircleFitting() {
        CircleScalar circle = new CircleScalar();
        circle.addPoint( 30.0,  68.0);
        circle.addPoint( 50.0,  -6.0);
        circle.addPoint(110.0, -20.0);
        circle.addPoint( 35.0,  15.0);
        circle.addPoint( 45.0,  97.0);
        // TODO: the wrapper around NonLinearConjugateGradientOptimizer is a temporary hack for
        // version 3.1 of the library. It should be removed when NonLinearConjugateGradientOptimizer
        // will officially be declared as implementing MultivariateDifferentiableOptimizer
        GradientMultivariateOptimizer underlying
            = new NonLinearConjugateGradientOptimizer(NonLinearConjugateGradientOptimizer.Formula.POLAK_RIBIERE,
                                                      new SimpleValueChecker(1e-10, 1e-10));
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(753289573253l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(new double[] { 50, 50 },
                                                    new double[] { 10, 10 },
                                                    new GaussianRandomGenerator(g));
        int nbStarts = 10;
        MultiStartMultivariateOptimizer optimizer
            = new MultiStartMultivariateOptimizer(underlying, nbStarts, generator);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(1000),
                                 circle.getObjectiveFunction(),
                                 circle.getObjectiveFunctionGradient(),
                                 GoalType.MINIMIZE,
                                 new InitialGuess(new double[] { 98.680, 47.345 }));
        Assert.assertEquals(1000, optimizer.getMaxEvaluations());
        PointValuePair[] optima = optimizer.getOptima();
        Assert.assertEquals(nbStarts, optima.length);
        for (PointValuePair o : optima) {
            // we check the results of all intermediate restarts here (there are 10 such results)
            Vector2D center = new Vector2D(o.getPointRef()[0], o.getPointRef()[1]);
            Assert.assertTrue(69.9592 < circle.getRadius(center));
            Assert.assertTrue(69.9602 > circle.getRadius(center));
            Assert.assertTrue(96.0745 < center.getX());
            Assert.assertTrue(96.0762 > center.getX());
            Assert.assertTrue(48.1344 < center.getY());
            Assert.assertTrue(48.1354 > center.getY());
        }

        Assert.assertTrue(optimizer.getEvaluations() > 850);
        Assert.assertTrue(optimizer.getEvaluations() < 900);

        Assert.assertEquals(3.1267527, optimum.getValue(), 1e-8);
    }

    @Test
    public void testRosenbrock() {
        Rosenbrock rosenbrock = new Rosenbrock();
        SimplexOptimizer underlying
            = new SimplexOptimizer(new SimpleValueChecker(-1, 1e-3));
        NelderMeadSimplex simplex = new NelderMeadSimplex(new double[][] {
                { -1.2,  1.0 },
                { 0.9, 1.2 } ,
                {  3.5, -2.3 }
            });
        JDKRandomGenerator g = new JDKRandomGenerator();
        g.setSeed(16069223052l);
        RandomVectorGenerator generator
            = new UncorrelatedRandomVectorGenerator(2, new GaussianRandomGenerator(g));
        int nbStarts = 10;
        MultiStartMultivariateOptimizer optimizer
            = new MultiStartMultivariateOptimizer(underlying, nbStarts, generator);
        PointValuePair optimum
            = optimizer.optimize(new MaxEval(1100),
                                 new ObjectiveFunction(rosenbrock),
                                 GoalType.MINIMIZE,
                                 simplex,
                                 new InitialGuess(new double[] { -1.2, 1.0 }));
        Assert.assertEquals(nbStarts, optimizer.getOptima().length);

        Assert.assertEquals(rosenbrock.getCount(), optimizer.getEvaluations());
        Assert.assertTrue(optimizer.getEvaluations() > 900);
        Assert.assertTrue(optimizer.getEvaluations() < 1200);
        Assert.assertTrue(optimum.getValue() < 5e-5);
    }

    private static class Rosenbrock implements MultivariateFunction {
        private int count;

        public Rosenbrock() {
            count = 0;
        }

        public double value(double[] x) {
            ++count;
            double a = x[1] - x[0] * x[0];
            double b = 1 - x[0];
            return 100 * a * a + b * b;
        }

        public int getCount() {
            return count;
        }
    }
}

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