| /* |
| * 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.general; |
| |
| import java.io.Serializable; |
| |
| import org.apache.commons.math3.analysis.differentiation.DerivativeStructure; |
| import org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableFunction; |
| import org.apache.commons.math3.analysis.solvers.BrentSolver; |
| import org.apache.commons.math3.geometry.euclidean.twod.Vector2D; |
| import org.apache.commons.math3.linear.BlockRealMatrix; |
| import org.apache.commons.math3.linear.RealMatrix; |
| import org.apache.commons.math3.optimization.GoalType; |
| import org.apache.commons.math3.optimization.PointValuePair; |
| import org.apache.commons.math3.optimization.SimpleValueChecker; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * <p>Some of the unit tests are re-implementations of the MINPACK <a |
| * href="http://www.netlib.org/minpack/ex/file17">file17</a> and <a |
| * href="http://www.netlib.org/minpack/ex/file22">file22</a> test files. |
| * The redistribution policy for MINPACK is available <a |
| * href="http://www.netlib.org/minpack/disclaimer">here</a>, for |
| * convenience, it is reproduced below.</p> |
| |
| * <table border="0" width="80%" cellpadding="10" align="center" bgcolor="#E0E0E0"> |
| * <tr><td> |
| * Minpack Copyright Notice (1999) University of Chicago. |
| * All rights reserved |
| * </td></tr> |
| * <tr><td> |
| * Redistribution and use in source and binary forms, with or without |
| * modification, are permitted provided that the following conditions |
| * are met: |
| * <ol> |
| * <li>Redistributions of source code must retain the above copyright |
| * notice, this list of conditions and the following disclaimer.</li> |
| * <li>Redistributions in binary form must reproduce the above |
| * copyright notice, this list of conditions and the following |
| * disclaimer in the documentation and/or other materials provided |
| * with the distribution.</li> |
| * <li>The end-user documentation included with the redistribution, if any, |
| * must include the following acknowledgment: |
| * <code>This product includes software developed by the University of |
| * Chicago, as Operator of Argonne National Laboratory.</code> |
| * Alternately, this acknowledgment may appear in the software itself, |
| * if and wherever such third-party acknowledgments normally appear.</li> |
| * <li><strong>WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS" |
| * WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE |
| * UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND |
| * THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES |
| * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE |
| * OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY |
| * OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR |
| * USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF |
| * THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4) |
| * DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION |
| * UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL |
| * BE CORRECTED.</strong></li> |
| * <li><strong>LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT |
| * HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF |
| * ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT, |
| * INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF |
| * ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF |
| * PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER |
| * SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT |
| * (INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE, |
| * EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE |
| * POSSIBILITY OF SUCH LOSS OR DAMAGES.</strong></li> |
| * <ol></td></tr> |
| * </table> |
| |
| * @author Argonne National Laboratory. MINPACK project. March 1980 (original fortran minpack tests) |
| * @author Burton S. Garbow (original fortran minpack tests) |
| * @author Kenneth E. Hillstrom (original fortran minpack tests) |
| * @author Jorge J. More (original fortran minpack tests) |
| * @author Luc Maisonobe (non-minpack tests and minpack tests Java translation) |
| */ |
| @Deprecated |
| public class NonLinearConjugateGradientOptimizerTest { |
| @Test |
| public void testTrivial() { |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 2 } }, new double[] { 3 }); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0 }); |
| Assert.assertEquals(1.5, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(0.0, optimum.getValue(), 1.0e-10); |
| } |
| |
| @Test |
| public void testColumnsPermutation() { |
| LinearProblem problem = |
| new LinearProblem(new double[][] { { 1.0, -1.0 }, { 0.0, 2.0 }, { 1.0, -2.0 } }, |
| new double[] { 4.0, 6.0, 1.0 }); |
| |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0 }); |
| Assert.assertEquals(7.0, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(0.0, optimum.getValue(), 1.0e-10); |
| |
| } |
| |
| @Test |
| public void testNoDependency() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 2, 0, 0, 0, 0, 0 }, |
| { 0, 2, 0, 0, 0, 0 }, |
| { 0, 0, 2, 0, 0, 0 }, |
| { 0, 0, 0, 2, 0, 0 }, |
| { 0, 0, 0, 0, 2, 0 }, |
| { 0, 0, 0, 0, 0, 2 } |
| }, new double[] { 0.0, 1.1, 2.2, 3.3, 4.4, 5.5 }); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 }); |
| for (int i = 0; i < problem.target.length; ++i) { |
| Assert.assertEquals(0.55 * i, optimum.getPoint()[i], 1.0e-10); |
| } |
| } |
| |
| @Test |
| public void testOneSet() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1, 0, 0 }, |
| { -1, 1, 0 }, |
| { 0, -1, 1 } |
| }, new double[] { 1, 1, 1}); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 }); |
| Assert.assertEquals(1.0, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals(2.0, optimum.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(3.0, optimum.getPoint()[2], 1.0e-10); |
| |
| } |
| |
| @Test |
| public void testTwoSets() { |
| final double epsilon = 1.0e-7; |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 2, 1, 0, 4, 0, 0 }, |
| { -4, -2, 3, -7, 0, 0 }, |
| { 4, 1, -2, 8, 0, 0 }, |
| { 0, -3, -12, -1, 0, 0 }, |
| { 0, 0, 0, 0, epsilon, 1 }, |
| { 0, 0, 0, 0, 1, 1 } |
| }, new double[] { 2, -9, 2, 2, 1 + epsilon * epsilon, 2}); |
| |
| final Preconditioner preconditioner |
| = new Preconditioner() { |
| public double[] precondition(double[] point, double[] r) { |
| double[] d = r.clone(); |
| d[0] /= 72.0; |
| d[1] /= 30.0; |
| d[2] /= 314.0; |
| d[3] /= 260.0; |
| d[4] /= 2 * (1 + epsilon * epsilon); |
| d[5] /= 4.0; |
| return d; |
| } |
| }; |
| |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-13, 1e-13), |
| new BrentSolver(), |
| preconditioner); |
| |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0, 0, 0, 0 }); |
| Assert.assertEquals( 3.0, optimum.getPoint()[0], 1.0e-10); |
| Assert.assertEquals( 4.0, optimum.getPoint()[1], 1.0e-10); |
| Assert.assertEquals(-1.0, optimum.getPoint()[2], 1.0e-10); |
| Assert.assertEquals(-2.0, optimum.getPoint()[3], 1.0e-10); |
| Assert.assertEquals( 1.0 + epsilon, optimum.getPoint()[4], 1.0e-10); |
| Assert.assertEquals( 1.0 - epsilon, optimum.getPoint()[5], 1.0e-10); |
| |
| } |
| |
| @Test |
| public void testNonInversible() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1, 2, -3 }, |
| { 2, 1, 3 }, |
| { -3, 0, -9 } |
| }, new double[] { 1, 1, 1 }); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 0, 0, 0 }); |
| Assert.assertTrue(optimum.getValue() > 0.5); |
| } |
| |
| @Test |
| public void testIllConditioned() { |
| LinearProblem problem1 = new LinearProblem(new double[][] { |
| { 10.0, 7.0, 8.0, 7.0 }, |
| { 7.0, 5.0, 6.0, 5.0 }, |
| { 8.0, 6.0, 10.0, 9.0 }, |
| { 7.0, 5.0, 9.0, 10.0 } |
| }, new double[] { 32, 23, 33, 31 }); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-13, 1e-13), |
| new BrentSolver(1e-15, 1e-15)); |
| PointValuePair optimum1 = |
| optimizer.optimize(200, problem1, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 }); |
| Assert.assertEquals(1.0, optimum1.getPoint()[0], 1.0e-4); |
| Assert.assertEquals(1.0, optimum1.getPoint()[1], 1.0e-4); |
| Assert.assertEquals(1.0, optimum1.getPoint()[2], 1.0e-4); |
| Assert.assertEquals(1.0, optimum1.getPoint()[3], 1.0e-4); |
| |
| LinearProblem problem2 = new LinearProblem(new double[][] { |
| { 10.00, 7.00, 8.10, 7.20 }, |
| { 7.08, 5.04, 6.00, 5.00 }, |
| { 8.00, 5.98, 9.89, 9.00 }, |
| { 6.99, 4.99, 9.00, 9.98 } |
| }, new double[] { 32, 23, 33, 31 }); |
| PointValuePair optimum2 = |
| optimizer.optimize(200, problem2, GoalType.MINIMIZE, new double[] { 0, 1, 2, 3 }); |
| Assert.assertEquals(-81.0, optimum2.getPoint()[0], 1.0e-1); |
| Assert.assertEquals(137.0, optimum2.getPoint()[1], 1.0e-1); |
| Assert.assertEquals(-34.0, optimum2.getPoint()[2], 1.0e-1); |
| Assert.assertEquals( 22.0, optimum2.getPoint()[3], 1.0e-1); |
| |
| } |
| |
| @Test |
| public void testMoreEstimatedParametersSimple() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 3.0, 2.0, 0.0, 0.0 }, |
| { 0.0, 1.0, -1.0, 1.0 }, |
| { 2.0, 0.0, 1.0, 0.0 } |
| }, new double[] { 7.0, 3.0, 5.0 }); |
| |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 7, 6, 5, 4 }); |
| Assert.assertEquals(0, optimum.getValue(), 1.0e-10); |
| |
| } |
| |
| @Test |
| public void testMoreEstimatedParametersUnsorted() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1.0, 1.0, 0.0, 0.0, 0.0, 0.0 }, |
| { 0.0, 0.0, 1.0, 1.0, 1.0, 0.0 }, |
| { 0.0, 0.0, 0.0, 0.0, 1.0, -1.0 }, |
| { 0.0, 0.0, -1.0, 1.0, 0.0, 1.0 }, |
| { 0.0, 0.0, 0.0, -1.0, 1.0, 0.0 } |
| }, new double[] { 3.0, 12.0, -1.0, 7.0, 1.0 }); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 2, 2, 2, 2, 2, 2 }); |
| Assert.assertEquals(0, optimum.getValue(), 1.0e-10); |
| } |
| |
| @Test |
| public void testRedundantEquations() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1.0, 1.0 }, |
| { 1.0, -1.0 }, |
| { 1.0, 3.0 } |
| }, new double[] { 3.0, 1.0, 5.0 }); |
| |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 1, 1 }); |
| Assert.assertEquals(2.0, optimum.getPoint()[0], 1.0e-8); |
| Assert.assertEquals(1.0, optimum.getPoint()[1], 1.0e-8); |
| |
| } |
| |
| @Test |
| public void testInconsistentEquations() { |
| LinearProblem problem = new LinearProblem(new double[][] { |
| { 1.0, 1.0 }, |
| { 1.0, -1.0 }, |
| { 1.0, 3.0 } |
| }, new double[] { 3.0, 1.0, 4.0 }); |
| |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-6, 1e-6)); |
| PointValuePair optimum = |
| optimizer.optimize(100, problem, GoalType.MINIMIZE, new double[] { 1, 1 }); |
| Assert.assertTrue(optimum.getValue() > 0.1); |
| |
| } |
| |
| @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); |
| NonLinearConjugateGradientOptimizer optimizer = |
| new NonLinearConjugateGradientOptimizer(ConjugateGradientFormula.POLAK_RIBIERE, |
| new SimpleValueChecker(1e-30, 1e-30), |
| new BrentSolver(1e-15, 1e-13)); |
| PointValuePair optimum = |
| optimizer.optimize(100, circle, GoalType.MINIMIZE, new double[] { 98.680, 47.345 }); |
| Vector2D center = new Vector2D(optimum.getPointRef()[0], optimum.getPointRef()[1]); |
| Assert.assertEquals(69.960161753, circle.getRadius(center), 1.0e-8); |
| Assert.assertEquals(96.075902096, center.getX(), 1.0e-8); |
| Assert.assertEquals(48.135167894, center.getY(), 1.0e-8); |
| } |
| |
| private static class LinearProblem implements MultivariateDifferentiableFunction, Serializable { |
| |
| private static final long serialVersionUID = 703247177355019415L; |
| final RealMatrix factors; |
| final double[] target; |
| public LinearProblem(double[][] factors, double[] target) { |
| this.factors = new BlockRealMatrix(factors); |
| this.target = target; |
| } |
| |
| public double value(double[] variables) { |
| double[] y = factors.operate(variables); |
| double sum = 0; |
| for (int i = 0; i < y.length; ++i) { |
| double ri = y[i] - target[i]; |
| sum += ri * ri; |
| } |
| return sum; |
| } |
| |
| public DerivativeStructure value(DerivativeStructure[] variables) { |
| DerivativeStructure[] y = new DerivativeStructure[factors.getRowDimension()]; |
| for (int i = 0; i < y.length; ++i) { |
| y[i] = variables[0].getField().getZero(); |
| for (int j = 0; j < factors.getColumnDimension(); ++j) { |
| y[i] = y[i].add(variables[j].multiply(factors.getEntry(i, j))); |
| } |
| } |
| |
| DerivativeStructure sum = variables[0].getField().getZero(); |
| for (int i = 0; i < y.length; ++i) { |
| DerivativeStructure ri = y[i].subtract(target[i]); |
| sum = sum.add(ri.multiply(ri)); |
| } |
| return sum; |
| } |
| |
| } |
| } |
