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/* ----------------------------------------------------------------------------
* 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 LevenbergMarquardtOptimizer.cpp
* @brief
* @author Richard Roberts
* @author Luca Carlone
* @date Feb 26, 2012
*/
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/linear/linearExceptions.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/VectorValues.h>
#include <gtsam/linear/Errors.h>
#include <boost/algorithm/string.hpp>
#include <boost/range/adaptor/map.hpp>
#include <string>
#include <cmath>
#include <fstream>
using namespace std;
namespace gtsam {
using boost::adaptors::map_values;
/* ************************************************************************* */
LevenbergMarquardtParams::VerbosityLM LevenbergMarquardtParams::verbosityLMTranslator(
const std::string &src) const {
std::string s = src;
boost::algorithm::to_upper(s);
if (s == "SILENT")
return LevenbergMarquardtParams::SILENT;
if (s == "LAMBDA")
return LevenbergMarquardtParams::LAMBDA;
if (s == "TRYLAMBDA")
return LevenbergMarquardtParams::TRYLAMBDA;
if (s == "TRYCONFIG")
return LevenbergMarquardtParams::TRYCONFIG;
if (s == "TRYDELTA")
return LevenbergMarquardtParams::TRYDELTA;
if (s == "DAMPED")
return LevenbergMarquardtParams::DAMPED;
/* default is silent */
return LevenbergMarquardtParams::SILENT;
}
/* ************************************************************************* */
std::string LevenbergMarquardtParams::verbosityLMTranslator(
VerbosityLM value) const {
std::string s;
switch (value) {
case LevenbergMarquardtParams::SILENT:
s = "SILENT";
break;
case LevenbergMarquardtParams::TERMINATION:
s = "TERMINATION";
break;
case LevenbergMarquardtParams::LAMBDA:
s = "LAMBDA";
break;
case LevenbergMarquardtParams::TRYLAMBDA:
s = "TRYLAMBDA";
break;
case LevenbergMarquardtParams::TRYCONFIG:
s = "TRYCONFIG";
break;
case LevenbergMarquardtParams::TRYDELTA:
s = "TRYDELTA";
break;
case LevenbergMarquardtParams::DAMPED:
s = "DAMPED";
break;
default:
s = "UNDEFINED";
break;
}
return s;
}
/* ************************************************************************* */
void LevenbergMarquardtParams::print(const std::string& str) const {
NonlinearOptimizerParams::print(str);
std::cout << " lambdaInitial: " << lambdaInitial << "\n";
std::cout << " lambdaFactor: " << lambdaFactor << "\n";
std::cout << " lambdaUpperBound: " << lambdaUpperBound << "\n";
std::cout << " lambdaLowerBound: " << lambdaLowerBound << "\n";
std::cout << " disableInnerIterations: " << disableInnerIterations
<< "\n";
std::cout << " minModelFidelity: " << minModelFidelity << "\n";
std::cout << " diagonalDamping: " << diagonalDamping << "\n";
std::cout << " verbosityLM: "
<< verbosityLMTranslator(verbosityLM) << "\n";
std::cout.flush();
}
/* ************************************************************************* */
GaussianFactorGraph::shared_ptr LevenbergMarquardtOptimizer::linearize() const {
return graph_.linearize(state_.values);
}
/* ************************************************************************* */
void LevenbergMarquardtOptimizer::increaseLambda() {
if (params_.useFixedLambdaFactor_) {
state_.lambda *= params_.lambdaFactor;
} else {
state_.lambda *= params_.lambdaFactor;
params_.lambdaFactor *= 2.0;
}
params_.reuse_diagonal_ = true;
}
/* ************************************************************************* */
void LevenbergMarquardtOptimizer::decreaseLambda(double stepQuality) {
if (params_.useFixedLambdaFactor_) {
state_.lambda /= params_.lambdaFactor;
} else {
// CHECK_GT(step_quality, 0.0);
state_.lambda *= std::max(1.0 / 3.0, 1.0 - pow(2.0 * stepQuality - 1.0, 3));
params_.lambdaFactor = 2.0;
}
state_.lambda = std::max(params_.lambdaLowerBound, state_.lambda);
params_.reuse_diagonal_ = false;
}
/* ************************************************************************* */
GaussianFactorGraph LevenbergMarquardtOptimizer::buildDampedSystem(
const GaussianFactorGraph& linear) {
//Set two parameters as Ceres, will move out later
static const double min_diagonal_ = 1e-6;
static const double max_diagonal_ = 1e32;
gttic(damp);
if (params_.verbosityLM >= LevenbergMarquardtParams::DAMPED)
cout << "building damped system with lambda " << state_.lambda << endl;
// Only retrieve diagonal vector when reuse_diagonal = false
if (params_.diagonalDamping && params_.reuse_diagonal_ == false) {
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), min_diagonal_), max_diagonal_);
v(aa) = sqrt(v(aa));
}
}
} // reuse diagonal
// for each of the variables, add a prior
double sigma = 1.0 / std::sqrt(state_.lambda);
GaussianFactorGraph damped = linear;
damped.reserve(damped.size() + state_.values.size());
if (params_.diagonalDamping) {
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));
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);
} catch (std::exception e) {
// Don't attempt any damping if no key found in diagonal
continue;
}
}
} else {
// Straightforward damping:
BOOST_FOREACH(const Values::KeyValuePair& key_value, state_.values) {
size_t dim = key_value.value.dim();
Matrix A = Matrix::Identity(dim, dim);
Vector b = Vector::Zero(dim);
SharedDiagonal model = noiseModel::Isotropic::Sigma(dim, sigma);
damped += boost::make_shared<JacobianFactor>(key_value.key, A, b, model);
}
}
gttoc(damp);
return damped;
}
/* ************************************************************************* */
void LevenbergMarquardtOptimizer::iterate() {
gttic(LM_iterate);
// Pull out parameters we'll use
const NonlinearOptimizerParams::Verbosity nloVerbosity = params_.verbosity;
const LevenbergMarquardtParams::VerbosityLM lmVerbosity = params_.verbosityLM;
// Linearize graph
if (lmVerbosity >= LevenbergMarquardtParams::DAMPED)
cout << "linearizing = " << endl;
GaussianFactorGraph::shared_ptr linear = linearize();
// Keep increasing lambda until we make make progress
while (true) {
if (lmVerbosity >= LevenbergMarquardtParams::TRYLAMBDA)
cout << "trying lambda = " << state_.lambda << endl;
// Build damped system for this lambda (adds prior factors that make it like gradient descent)
GaussianFactorGraph dampedSystem = buildDampedSystem(*linear);
// Log current error/lambda to file
if (!params_.logFile.empty()) {
ofstream os(params_.logFile.c_str(), ios::app);
boost::posix_time::ptime currentTime =
boost::posix_time::microsec_clock::universal_time();
os << state_.totalNumberInnerIterations << ","
<< 1e-6 * (currentTime - state_.startTime).total_microseconds() << ","
<< state_.error << "," << state_.lambda << endl;
}
++state_.totalNumberInnerIterations;
// Try solving
double modelFidelity = 0.0;
bool step_is_successful = false;
bool stopSearchingLambda = false;
double newError;
Values newValues;
VectorValues delta;
bool systemSolvedSuccessfully;
try {
delta = solve(dampedSystem, state_.values, params_);
systemSolvedSuccessfully = true;
} catch (IndeterminantLinearSystemException& e) {
systemSolvedSuccessfully = false;
}
if (systemSolvedSuccessfully) {
params_.reuse_diagonal_ = true;
if (lmVerbosity >= LevenbergMarquardtParams::TRYLAMBDA)
cout << "linear delta norm = " << delta.norm() << endl;
if (lmVerbosity >= LevenbergMarquardtParams::TRYDELTA)
delta.print("delta");
// cost change in the linearized system (old - new)
double newlinearizedError = linear->error(delta);
double linearizedCostChange = state_.error - newlinearizedError;
if (linearizedCostChange >= 0) { // step is valid
// update values
gttic(retract);
newValues = state_.values.retract(delta);
gttoc(retract);
// compute new error
gttic(compute_error);
if (lmVerbosity >= LevenbergMarquardtParams::TRYLAMBDA)
cout << "calculating error" << endl;
newError = graph_.error(newValues);
gttoc(compute_error);
// 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) {
// 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
}
} else {
stopSearchingLambda = true;
}
}
}
if (step_is_successful) { // we have successfully decreased the cost and we have good modelFidelity
state_.values.swap(newValues);
state_.error = newError;
decreaseLambda(modelFidelity);
break;
} else if (!stopSearchingLambda) { // we failed to solved the system or we had no decrease in cost
if (lmVerbosity >= LevenbergMarquardtParams::TRYLAMBDA)
cout << "increasing lambda: old error (" << state_.error
<< ") new error (" << newError << ")" << endl;
increaseLambda();
// check if lambda is too big
if (state_.lambda >= params_.lambdaUpperBound) {
if (nloVerbosity >= NonlinearOptimizerParams::TERMINATION)
cout
<< "Warning: Levenberg-Marquardt giving up because cannot decrease error with maximum lambda"
<< endl;
break;
}
} else { // the change in the cost is very small and it is not worth trying bigger lambdas
break;
}
} // end while
// Increment the iteration counter
++state_.iterations;
}
/* ************************************************************************* */
LevenbergMarquardtParams LevenbergMarquardtOptimizer::ensureHasOrdering(
LevenbergMarquardtParams params, const NonlinearFactorGraph& graph) const {
if (!params.ordering)
params.ordering = Ordering::COLAMD(graph);
return params;
}
} /* namespace gtsam */
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