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Wrapped DoglegOptimizer and LevenbergMarquardtOptimizer, added class heirarchy to optimization parameters

release/4.3a0
Richard Roberts 2012-07-18 23:35:36 +00:00
parent 11981dd828
commit 9dadbebbd1
3 changed files with 98 additions and 21 deletions
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85
gtsam.h
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@ -1130,32 +1130,46 @@ class Marginals {
Matrix marginalInformation(size_t variable) const;
};
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
class LevenbergMarquardtParams {
LevenbergMarquardtParams();
void print(string s) const;
//*************************************************************************
// Nonlinear optimizers
//*************************************************************************
size_t getMaxIterations() const;
double getRelativeErrorTol() const;
double getAbsoluteErrorTol() const;
double getErrorTol() const;
string getVerbosity() const;
#include <gtsam/nonlinear/NonlinearOptimizer.h>
virtual class NonlinearOptimizerParams {
NonlinearOptimizerParams();
void print(string s) const;
void setMaxIterations(size_t value);
void setRelativeErrorTol(double value);
void setAbsoluteErrorTol(double value);
void setErrorTol(double value);
void setVerbosity(string s);
size_t getMaxIterations() const;
double getRelativeErrorTol() const;
double getAbsoluteErrorTol() const;
double getErrorTol() const;
string getVerbosity() const;
void setMaxIterations(size_t value);
void setRelativeErrorTol(double value);
void setAbsoluteErrorTol(double value);
void setErrorTol(double value);
void setVerbosity(string s);
};
#include <gtsam/nonlinear/SuccessiveLinearizationOptimizer.h>
virtual class SuccessiveLinearizationParams : gtsam::NonlinearOptimizerParams {
SuccessiveLinearizationParams();
bool isMultifrontal() const;
bool isSequential() const;
bool isCholmod() const;
bool isCG() const;
};
double getlambdaInitial() const ;
double getlambdaFactor() const ;
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
virtual class LevenbergMarquardtParams : gtsam::SuccessiveLinearizationParams {
LevenbergMarquardtParams();
double getlambdaInitial() const;
double getlambdaFactor() const;
double getlambdaUpperBound() const;
string getVerbosityLM() const ;
string getVerbosityLM() const;
void setlambdaInitial(double value);
void setlambdaFactor(double value);
@ -1163,22 +1177,53 @@ class LevenbergMarquardtParams {
void setVerbosityLM(string s);
};
#include <gtsam/nonlinear/DoglegOptimizer.h>
virtual class DoglegParams : gtsam::SuccessiveLinearizationParams {
DoglegParams();
double getDeltaInitial() const;
string getVerbosityDL() const;
void setDeltaInitial(double deltaInitial) const;
void setVerbosityDL(string verbosityDL) const;
};
virtual class NonlinearOptimizer {
gtsam::Values optimizeSafely();
double error() const;
int iterations() const;
gtsam::Values values() const;
void iterate() const;
};
virtual class DoglegOptimizer : gtsam::NonlinearOptimizer {
DoglegOptimizer(const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& initialValues);
DoglegOptimizer(const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& initialValues, const gtsam::DoglegParams& params);
double getDelta() const;
};
virtual class LevenbergMarquardtOptimizer : gtsam::NonlinearOptimizer {
LevenbergMarquardtOptimizer(const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& initialValues);
LevenbergMarquardtOptimizer(const gtsam::NonlinearFactorGraph& graph, const gtsam::Values& initialValues, const gtsam::LevenbergMarquardtParams& params);
double lambda() const;
};
//*************************************************************************
// Nonlinear factor types
//*************************************************************************
template<T = {gtsam::LieVector, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2, gtsam::CalibratedCamera, gtsam::SimpleCamera}>
template<T = {gtsam::LieVector, gtsam::LieMatrix, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2, gtsam::CalibratedCamera, gtsam::SimpleCamera}>
virtual class PriorFactor : gtsam::NonlinearFactor {
PriorFactor(size_t key, const T& prior, const gtsam::noiseModel::Base* noiseModel);
};
template<T = {gtsam::LieVector, gtsam::Point2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3}>
template<T = {gtsam::LieVector, gtsam::LieMatrix, gtsam::Point2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3}>
virtual class BetweenFactor : gtsam::NonlinearFactor {
BetweenFactor(size_t key1, size_t key2, const T& relativePose, const gtsam::noiseModel::Base* noiseModel);
};
template<T = {gtsam::LieVector, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2, gtsam::CalibratedCamera, gtsam::SimpleCamera}>
template<T = {gtsam::LieVector, gtsam::LieMatrix, gtsam::Point2, gtsam::StereoPoint2, gtsam::Point3, gtsam::Rot2, gtsam::Rot3, gtsam::Pose2, gtsam::Pose3, gtsam::Cal3_S2, gtsam::CalibratedCamera, gtsam::SimpleCamera}>
virtual class NonlinearEquality : gtsam::NonlinearFactor {
// Constructor - forces exact evaluation
NonlinearEquality(size_t j, const T& feasible);

23
gtsam/nonlinear/DoglegOptimizer.cpp
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@ -22,10 +22,33 @@
#include <gtsam/linear/GaussianJunctionTree.h>
#include <gtsam/nonlinear/DoglegOptimizerImpl.h>
#include <boost/algorithm/string.hpp>
using namespace std;
namespace gtsam {
/* ************************************************************************* */
DoglegParams::VerbosityDL DoglegParams::verbosityDLTranslator(const std::string &verbosityDL) const {
std::string s = verbosityDL; boost::algorithm::to_upper(s);
if (s == "SILENT") return DoglegParams::SILENT;
if (s == "VERBOSE") return DoglegParams::VERBOSE;
/* default is silent */
return DoglegParams::SILENT;
}
/* ************************************************************************* */
std::string DoglegParams::verbosityDLTranslator(VerbosityDL verbosityDL) const {
std::string s;
switch (verbosityDL) {
case DoglegParams::SILENT: s = "SILENT"; break;
case DoglegParams::VERBOSE: s = "VERBOSE"; break;
default: s = "UNDEFINED"; break;
}
return s;
}
/* ************************************************************************* */
void DoglegOptimizer::iterate(void) {

11
gtsam/nonlinear/DoglegOptimizer.h
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@ -50,6 +50,15 @@ public:
std::cout << " deltaInitial: " << deltaInitial << "\n";
std::cout.flush();
}
double getDeltaInitial() const { return deltaInitial; }
std::string getVerbosityDL() const { return verbosityDLTranslator(verbosityDL); }
void setDeltaInitial(double deltaInitial) { this->deltaInitial = deltaInitial; }
void setVerbosityDL(const std::string& verbosityDL) { this->verbosityDL = verbosityDLTranslator(verbosityDL); }
VerbosityDL verbosityDLTranslator(const std::string& verbosityDL) const;
std::string verbosityDLTranslator(VerbosityDL verbosityDL) const;
};
/**
@ -128,7 +137,7 @@ public:
const DoglegState& state() const { return state_; }
/** Access the current trust region radius Delta */
double Delta() const { return state_.Delta; }
double getDelta() const { return state_.Delta; }
/// @}