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