| /* |
| * 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.math4.optim.nonlinear.scalar; |
| |
| import org.apache.commons.geometry.euclidean.twod.Vector2D; |
| import org.apache.commons.math4.analysis.MultivariateFunction; |
| import org.apache.commons.math4.optim.InitialGuess; |
| import org.apache.commons.math4.optim.MaxEval; |
| import org.apache.commons.math4.optim.PointValuePair; |
| import org.apache.commons.math4.optim.SimpleValueChecker; |
| import org.apache.commons.math4.optim.nonlinear.scalar.gradient.CircleScalar; |
| import org.apache.commons.math4.optim.nonlinear.scalar.gradient.NonLinearConjugateGradientOptimizer; |
| import org.apache.commons.math4.optim.nonlinear.scalar.noderiv.NelderMeadSimplex; |
| import org.apache.commons.math4.optim.nonlinear.scalar.noderiv.SimplexOptimizer; |
| import org.apache.commons.rng.UniformRandomProvider; |
| import org.apache.commons.rng.simple.RandomSource; |
| import org.apache.commons.math4.random.GaussianRandomGenerator; |
| import org.apache.commons.math4.random.RandomVectorGenerator; |
| import org.apache.commons.math4.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)); |
| UniformRandomProvider g = RandomSource.create(RandomSource.MT_64, 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 = Vector2D.of(o.getPointRef()[0], o.getPointRef()[1]); |
| Assert.assertEquals(69.9597, circle.getRadius(center), 1e-3); |
| Assert.assertEquals(96.07535, center.getX(), 1.4e-3); |
| Assert.assertEquals(48.1349, center.getY(), 5e-3); |
| } |
| |
| Assert.assertTrue(optimizer.getEvaluations() > 800); |
| 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 } |
| }); |
| // The test is extremely sensitive to the seed. |
| UniformRandomProvider g = RandomSource.create(RandomSource.MT_64, 16069223056L); |
| 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); |
| |
| final int numEval = optimizer.getEvaluations(); |
| Assert.assertEquals(rosenbrock.getCount(), numEval); |
| Assert.assertTrue("numEval=" + numEval, numEval > 900); |
| Assert.assertTrue("numEval=" + numEval, numEval < 1200); |
| Assert.assertTrue("optimum=" + optimum.getValue(), optimum.getValue() < 5e-5); |
| } |
| |
| private static class Rosenbrock implements MultivariateFunction { |
| private int count; |
| |
| public Rosenbrock() { |
| count = 0; |
| } |
| |
| @Override |
| 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; |
| } |
| } |
| } |