12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Much more extensive testing of diagonal policy with Luca over Skype - gets stuck in a non-minimum !!!

release/4.3a0
Frank Dellaert 2014-02-20 00:28:15 -05:00
parent 666072b169
commit 33ae83fcb2
1 changed files with 62 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

65
tests/testNonlinearOptimizer.cpp
View File

@ -237,9 +237,12 @@ TEST(NonlinearOptimizer, NullFactor) {
TEST(NonlinearOptimizer, MoreOptimization) { TEST(NonlinearOptimizer, MoreOptimization) {
NonlinearFactorGraph fg; NonlinearFactorGraph fg;
fg += PriorFactor<Pose2>(0, Pose2(0,0,0), noiseModel::Isotropic::Sigma(3,1)); fg += PriorFactor<Pose2>(0, Pose2(0, 0, 0),
fg += BetweenFactor<Pose2>(0, 1, Pose2(1,0,M_PI/2), noiseModel::Isotropic::Sigma(3,1)); noiseModel::Isotropic::Sigma(3, 1));
fg += BetweenFactor<Pose2>(1, 2, Pose2(1,0,M_PI/2), noiseModel::Isotropic::Sigma(3,1)); fg += BetweenFactor<Pose2>(0, 1, Pose2(1, 0, M_PI / 2),
noiseModel::Isotropic::Sigma(3, 1));
fg += BetweenFactor<Pose2>(1, 2, Pose2(1, 0, M_PI / 2),
noiseModel::Isotropic::Sigma(3, 1));
Values init; Values init;
init.insert(0, Pose2(3, 4, -M_PI)); init.insert(0, Pose2(3, 4, -M_PI));
@ -251,20 +254,60 @@ TEST(NonlinearOptimizer, MoreOptimization) {
expected.insert(1, Pose2(1, 0, M_PI / 2)); expected.insert(1, Pose2(1, 0, M_PI / 2));
expected.insert(2, Pose2(1, 1, M_PI)); expected.insert(2, Pose2(1, 1, M_PI));
VectorValues expectedGradient;
expectedGradient.insert(0,zero(3));
expectedGradient.insert(1,zero(3));
expectedGradient.insert(2,zero(3));
// Try LM and Dogleg // Try LM and Dogleg
EXPECT(assert_equal(expected, LevenbergMarquardtOptimizer(fg, init).optimize())); LevenbergMarquardtParams params;
// params.setVerbosityLM("TRYDELTA");
// params.setVerbosity("TERMINATION");
params.setlambdaUpperBound(1e9);
params.setRelativeErrorTol(0);
params.setAbsoluteErrorTol(0);
//params.setlambdaInitial(10);
{
LevenbergMarquardtOptimizer optimizer(fg, init, params);
// test convergence
Values actual = optimizer.optimize();
EXPECT(assert_equal(expected, actual));
// Check that the gradient is zero
GaussianFactorGraph::shared_ptr linear = optimizer.linearize();
EXPECT(assert_equal(expectedGradient,linear->gradientAtZero()));
}
EXPECT(assert_equal(expected, DoglegOptimizer(fg, init).optimize())); EXPECT(assert_equal(expected, DoglegOptimizer(fg, init).optimize()));
// Try LM with diagonal damping cout << "===================================================================================" << endl;
Values initBetter;
initBetter.insert(0, Pose2(3,4,0));
initBetter.insert(1, Pose2(10,2,M_PI/3));
initBetter.insert(2, Pose2(11,7,M_PI/2));
LevenbergMarquardtParams params; // Try LM with diagonal damping
Values initBetter = init;
// initBetter.insert(0, Pose2(3,4,0));
// initBetter.insert(1, Pose2(10,2,M_PI/3));
// initBetter.insert(2, Pose2(11,7,M_PI/2));
{
params.setDiagonalDamping(true); params.setDiagonalDamping(true);
LevenbergMarquardtOptimizer optimizer(fg, initBetter, params); LevenbergMarquardtOptimizer optimizer(fg, initBetter, params);
EXPECT(assert_equal(expected, optimizer.optimize()));
// test the diagonal
GaussianFactorGraph::shared_ptr linear = optimizer.linearize();
GaussianFactorGraph damped = optimizer.buildDampedSystem(*linear);
VectorValues d = linear->hessianDiagonal(), //
expectedDiagonal = d + params.lambdaInitial * d;
EXPECT(assert_equal(expectedDiagonal, damped.hessianDiagonal()));
// test convergence
Values actual = optimizer.optimize();
EXPECT(assert_equal(expected, actual));
// Check that the gradient is zero
linear = optimizer.linearize();
EXPECT(assert_equal(expectedGradient,linear->gradientAtZero()));
}
} }
/* ************************************************************************* */ /* ************************************************************************* */