gtsam/cpp/testNonlinearOptimizer.cpp

141 lines
3.7 KiB
C++

/**
* @file testNonlinearOptimizer.cpp
* @brief Unit tests for NonlinearOptimizer class
* @author Frank Dellaert
*/
#include <iostream>
using namespace std;
#include <CppUnitLite/TestHarness.h>
#include "Matrix.h"
#include "smallExample.h"
// template definitions
#include "NonlinearOptimizer-inl.h"
using namespace gtsam;
typedef NonlinearOptimizer<NonlinearFactorGraph,FGConfig> Optimizer;
/* ************************************************************************* */
TEST( NonlinearFactorGraph, delta )
{
NonlinearFactorGraph fg = createNonlinearFactorGraph();
Optimizer::shared_config initial = sharedNoisyConfig();
// Expected configuration is the difference between the noisy config
// and the ground-truth config. One step only because it's linear !
FGConfig expected;
Vector dl1(2);
dl1(0) = -0.1;
dl1(1) = 0.1;
expected.insert("l1", dl1);
Vector dx1(2);
dx1(0) = -0.1;
dx1(1) = -0.1;
expected.insert("x1", dx1);
Vector dx2(2);
dx2(0) = 0.1;
dx2(1) = -0.2;
expected.insert("x2", dx2);
// Check one ordering
Ordering ord1;
ord1.push_back("x2");
ord1.push_back("l1");
ord1.push_back("x1");
Optimizer optimizer1(fg, ord1, initial);
FGConfig actual1 = optimizer1.delta();
CHECK(assert_equal(actual1,expected));
// Check another
Ordering ord2;
ord2.push_back("x1");
ord2.push_back("x2");
ord2.push_back("l1");
Optimizer optimizer2(fg, ord2, initial);
FGConfig actual2 = optimizer2.delta();
CHECK(assert_equal(actual2,expected));
// And yet another...
Ordering ord3;
ord3.push_back("l1");
ord3.push_back("x1");
ord3.push_back("x2");
Optimizer optimizer3(fg, ord3, initial);
FGConfig actual3 = optimizer3.delta();
CHECK(assert_equal(actual3,expected));
}
/* ************************************************************************* */
TEST( NonlinearFactorGraph, iterateLM )
{
// really non-linear factor graph
NonlinearFactorGraph fg = createReallyNonlinearFactorGraph();
// config far from minimum
Vector x0 = Vector_(1, 3.0);
boost::shared_ptr<FGConfig> config(new FGConfig);
config->insert("x", x0);
// ordering
Ordering ord;
ord.push_back("x");
// create initial optimization state, with lambda=0
Optimizer optimizer(fg, ord, config, 0);
// normal iterate
Optimizer iterated1 = optimizer.iterate();
// LM iterate with lambda 0 should be the same
Optimizer iterated2 = optimizer.iterateLM();
CHECK(assert_equal(*(iterated1.config()), *(iterated2.config()), 1e-9));
}
/* ************************************************************************* */
TEST( NonlinearFactorGraph, optimize )
{
NonlinearFactorGraph fg = createReallyNonlinearFactorGraph();
// test error at minimum
Vector xstar = Vector_(1, 0.0);
FGConfig cstar;
cstar.insert("x", xstar);
DOUBLES_EQUAL(0.0,fg.error(cstar),0.0);
// test error at initial = [(1-cos(3))^2 + (sin(3))^2]*50 =
Vector x0 = Vector_(1, 3.0);
boost::shared_ptr<FGConfig> c0(new FGConfig);
c0->insert("x", x0);
DOUBLES_EQUAL(199.0,fg.error(*c0),1e-3);
// optimize parameters
Ordering ord;
ord.push_back("x");
double relativeThreshold = 1e-5;
double absoluteThreshold = 1e-5;
// initial optimization state is the same in both cases tested
Optimizer optimizer(fg, ord, c0);
// Gauss-Newton
Optimizer actual1 = optimizer.gaussNewton(relativeThreshold,
absoluteThreshold);
CHECK(assert_equal(*(actual1.config()),cstar));
// Levenberg-Marquardt
Optimizer actual2 = optimizer.levenbergMarquardt(relativeThreshold,
absoluteThreshold, Optimizer::SILENT);
CHECK(assert_equal(*(actual2.config()),cstar));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */