153 lines
6.3 KiB
C++
153 lines
6.3 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file DoglegOptimizer.h
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* @brief Unit tests for DoglegOptimizer
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* @author Richard Roberts
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <tests/smallExample.h>
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#include <gtsam/nonlinear/DoglegOptimizerImpl.h>
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/linear/JacobianFactor.h>
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#include <gtsam/linear/GaussianBayesTree.h>
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#include <gtsam/base/numericalDerivative.h>
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#ifdef __GNUC__
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wunused-variable"
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#endif
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#include <boost/bind.hpp>
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#ifdef __GNUC__
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#pragma GCC diagnostic pop
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#endif
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#include <boost/assign/list_of.hpp> // for 'list_of()'
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#include <functional>
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#include <boost/iterator/counting_iterator.hpp>
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using namespace std;
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using namespace gtsam;
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// Convenience for named keys
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using symbol_shorthand::X;
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using symbol_shorthand::L;
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/* ************************************************************************* */
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TEST(DoglegOptimizer, ComputeBlend) {
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// Create an arbitrary Bayes Net
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GaussianBayesNet gbn;
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
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3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
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4, Matrix_(2,2, 11.0,12.0,13.0,14.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
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2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
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4, Matrix_(2,2, 25.0,26.0,27.0,28.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
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3, Matrix_(2,2, 35.0,36.0,37.0,38.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
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4, Matrix_(2,2, 45.0,46.0,47.0,48.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0)));
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// Compute steepest descent point
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VectorValues xu = gbn.optimizeGradientSearch();
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// Compute Newton's method point
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VectorValues xn = gbn.optimize();
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// The Newton's method point should be more "adventurous", i.e. larger, than the steepest descent point
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EXPECT(xu.vector().norm() < xn.vector().norm());
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// Compute blend
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double Delta = 1.5;
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VectorValues xb = DoglegOptimizerImpl::ComputeBlend(Delta, xu, xn);
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DOUBLES_EQUAL(Delta, xb.vector().norm(), 1e-10);
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}
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/* ************************************************************************* */
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TEST(DoglegOptimizer, ComputeDoglegPoint) {
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// Create an arbitrary Bayes Net
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GaussianBayesNet gbn;
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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0, Vector_(2, 1.0,2.0), Matrix_(2,2, 3.0,4.0,0.0,6.0),
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3, Matrix_(2,2, 7.0,8.0,9.0,10.0),
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4, Matrix_(2,2, 11.0,12.0,13.0,14.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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1, Vector_(2, 15.0,16.0), Matrix_(2,2, 17.0,18.0,0.0,20.0),
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2, Matrix_(2,2, 21.0,22.0,23.0,24.0),
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4, Matrix_(2,2, 25.0,26.0,27.0,28.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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2, Vector_(2, 29.0,30.0), Matrix_(2,2, 31.0,32.0,0.0,34.0),
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3, Matrix_(2,2, 35.0,36.0,37.0,38.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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3, Vector_(2, 39.0,40.0), Matrix_(2,2, 41.0,42.0,0.0,44.0),
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4, Matrix_(2,2, 45.0,46.0,47.0,48.0)));
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gbn += GaussianConditional::shared_ptr(new GaussianConditional(
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4, Vector_(2, 49.0,50.0), Matrix_(2,2, 51.0,52.0,0.0,54.0)));
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// Compute dogleg point for different deltas
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double Delta1 = 0.5; // Less than steepest descent
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VectorValues actual1 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta1, gbn.optimizeGradientSearch(), gbn.optimize());
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DOUBLES_EQUAL(Delta1, actual1.vector().norm(), 1e-5);
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double Delta2 = 1.5; // Between steepest descent and Newton's method
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VectorValues expected2 = DoglegOptimizerImpl::ComputeBlend(Delta2, gbn.optimizeGradientSearch(), gbn.optimize());
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VectorValues actual2 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta2, gbn.optimizeGradientSearch(), gbn.optimize());
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DOUBLES_EQUAL(Delta2, actual2.vector().norm(), 1e-5);
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EXPECT(assert_equal(expected2, actual2));
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double Delta3 = 5.0; // Larger than Newton's method point
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VectorValues expected3 = gbn.optimize();
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VectorValues actual3 = DoglegOptimizerImpl::ComputeDoglegPoint(Delta3, gbn.optimizeGradientSearch(), gbn.optimize());
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EXPECT(assert_equal(expected3, actual3));
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}
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/* ************************************************************************* */
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TEST(DoglegOptimizer, Iterate) {
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// really non-linear factor graph
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NonlinearFactorGraph fg = example::createReallyNonlinearFactorGraph();
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// config far from minimum
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Point2 x0(3,0);
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Values config;
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config.insert(X(1), x0);
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double Delta = 1.0;
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for(size_t it=0; it<10; ++it) {
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GaussianBayesNet gbn = *fg.linearize(config)->eliminateSequential();
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// Iterate assumes that linear error = nonlinear error at the linearization point, and this should be true
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double nonlinearError = fg.error(config);
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double linearError = GaussianFactorGraph(gbn).error(config.zeroVectors());
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DOUBLES_EQUAL(nonlinearError, linearError, 1e-5);
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// cout << "it " << it << ", Delta = " << Delta << ", error = " << fg->error(*config) << endl;
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VectorValues dx_u = gbn.optimizeGradientSearch();
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VectorValues dx_n = gbn.optimize();
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DoglegOptimizerImpl::IterationResult result = DoglegOptimizerImpl::Iterate(Delta, DoglegOptimizerImpl::SEARCH_EACH_ITERATION, dx_u, dx_n, gbn, fg, config, fg.error(config));
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Delta = result.Delta;
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EXPECT(result.f_error < fg.error(config)); // Check that error decreases
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Values newConfig(config.retract(result.dx_d));
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config = newConfig;
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DOUBLES_EQUAL(fg.error(config), result.f_error, 1e-5); // Check that error is correctly filled in
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}
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}
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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