/*
* Copyright (c) 2012-2015, Peter Abeles. All Rights Reserved.
*
* This file is part of DDogleg (http://ddogleg.org).
*
* Licensed 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.ddogleg.optimization.impl;
import org.ddogleg.optimization.EvaluateQuasiNewtonBFGS;
import org.ddogleg.optimization.LineSearch;
import org.ddogleg.optimization.NonlinearResults;
import org.ddogleg.optimization.functions.FunctionNtoS;
import org.ddogleg.optimization.functions.GradientLineFunction;
import org.ddogleg.optimization.wrap.CachedNumericalGradientLineFunction;
import org.junit.Test;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertTrue;
/**
* @author Peter Abeles
*/
public class TestQuasiNewtonBFGS {
EvaluateQuasiNewtonBFGS evaluator = new EvaluateQuasiNewtonBFGS(false,false);
/**
* Basic test that is easily solved.
*/
@Test
public void basicTest() {
QuasiNewtonBFGS alg = createAlg(new TrivialFunctionNtoS());
alg.initialize(new double[]{1,1,1});
int i = 0;
for( ; i < 200 && !alg.iterate(); i++ ){}
assertTrue(alg.isConverged());
double[] found = alg.getParameters();
assertEquals(0, found[0], 1e-4);
assertEquals(0, found[1], 1e-4);
assertEquals(1, found[2], 1e-4); // no change expected in last parameter
}
public QuasiNewtonBFGS createAlg( FunctionNtoS function ) {
double gtol = 0.9;
LineSearch lineSearch = new LineSearchMore94().setConvergence(1e-3,gtol,0.1);
GradientLineFunction f = new CachedNumericalGradientLineFunction(function);
QuasiNewtonBFGS alg = new QuasiNewtonBFGS(f,lineSearch,0);
alg.setConvergence(1e-7,1e-7,gtol);
return alg;
}
@Test
public void powell() {
NonlinearResults results = evaluator.powell();
// no algorithm to compare it against, just do some sanity checks for changes
assertTrue(results.numFunction < 300);
assertTrue(results.numGradient < 300);
// The function is degenerate, this test sees if it converges to a solution and improves
// the parameter values. It isn't very precise
// assertEquals(0,results.x[0],1e-4);
// assertEquals(0,results.x[1],1e-4);
assertEquals(0, results.f, 1e-4);
}
@Test
public void helicalvalley() {
NonlinearResults results = evaluator.helicalValley();
// no algorithm to compare it against, just do some sanity checks for changes
assertTrue(results.numFunction < 100);
assertTrue(results.numGradient < 100);
assertEquals(1, results.x[0], 1e-4);
assertEquals(0, results.x[1], 1e-4);
assertEquals(0, results.x[2], 1e-4);
assertEquals(0, results.f, 1e-4);
}
@Test
public void rosenbrock() {
NonlinearResults results = evaluator.rosenbrock();
// no algorithm to compare it against, just do some sanity checks for changes
assertTrue(results.numFunction < 100);
assertTrue(results.numGradient < 100);
assertEquals(1, results.x[0], 1e-4);
assertEquals(1, results.x[1], 1e-4);
assertEquals(0, results.f, 1e-4);
}
@Test
public void badlyScaledBrown() {
NonlinearResults results = evaluator.badlyScaledBrown();
// no algorithm to compare it against, just do some sanity checks for changes
assertTrue(results.numFunction < 100);
assertTrue(results.numGradient < 100);
assertEquals(1e6, results.x[0], 1e-4);
assertEquals(2e-6, results.x[1], 1e-4);
assertEquals(0, results.f, 1e-4);
}
@Test
public void trigonometric() {
NonlinearResults results = evaluator.trigonometric();
// no algorithm to compare it against, just do some sanity checks for changes
assertTrue(results.numFunction < 100);
assertTrue(results.numGradient < 100);
assertEquals(0, results.f, 1e-4);
}
}
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