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when relative decrease is small and positive, the step is applied before termination

release/4.3a0
Luca 2014-03-17 14:20:41 -04:00
parent 3ee404a5a6
commit 5f8f38a8e0
1 changed files with 17 additions and 14 deletions
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31
gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
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@ -148,7 +148,8 @@ GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
state_.hessianDiagonal = linear.hessianDiagonal();
BOOST_FOREACH(Vector& v, state_.hessianDiagonal | map_values) {
for (int aa = 0; aa < v.size(); aa++) {
v(aa) = std::min(std::max(v(aa), params_.min_diagonal_), params_.max_diagonal_);
v(aa) = std::min(std::max(v(aa), params_.min_diagonal_),
params_.max_diagonal_);
v(aa) = sqrt(v(aa));
}
}
@ -162,11 +163,13 @@ GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
BOOST_FOREACH(const VectorValues::KeyValuePair& key_vector, state_.hessianDiagonal) {
// Fill in the diagonal of A with diag(hessian)
try {
Matrix A = Eigen::DiagonalMatrix<double, Eigen::Dynamic>(state_.hessianDiagonal.at(key_vector.first));
Matrix A = Eigen::DiagonalMatrix<double, Eigen::Dynamic>(
state_.hessianDiagonal.at(key_vector.first));
size_t dim = key_vector.second.size();
Vector b = Vector::Zero(dim);
SharedDiagonal model = noiseModel::Isotropic::Sigma(dim, sigma);
damped += boost::make_shared<JacobianFactor>(key_vector.first, A, b, model);
damped += boost::make_shared<JacobianFactor>(key_vector.first, A, b,
model);
} catch (std::exception e) {
// Don't attempt any damping if no key found in diagonal
continue;
@ -268,20 +271,20 @@ void LevenbergMarquardtOptimizer::iterate() {
// cost change in the original, nonlinear system (old - new)
double costChange = state_.error - newError;
double absolute_function_tolerance = params_.relativeErrorTol
* state_.error;
if (fabs(costChange) >= absolute_function_tolerance) {
if (linearizedCostChange > 1e-15) { // the error has to decrease to satify this condition
// fidelity of linearized model VS original system between
if (linearizedCostChange > 1e-15) {
modelFidelity = costChange / linearizedCostChange;
// if we decrease the error in the nonlinear system and modelFidelity is above threshold
step_is_successful = modelFidelity > params_.minModelFidelity;
} else {
step_is_successful = true; // linearizedCostChange close to zero
}
modelFidelity = costChange / linearizedCostChange;
// if we decrease the error in the nonlinear system and modelFidelity is above threshold
step_is_successful = modelFidelity > params_.minModelFidelity;
} else {
stopSearchingLambda = true;
step_is_successful = true; // linearizedCostChange close to zero
}
double minAbsoluteTolerance = params_.relativeErrorTol * state_.error;
// if the change is small we terminate
if (fabs(costChange) < minAbsoluteTolerance)
stopSearchingLambda = true;
}
}