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TWo default param sets

release/4.3a0
Frank Dellaert 2015-06-18 09:51:22 -07:00
parent 9f209e441f
commit 879e66a63a
2 changed files with 85 additions and 74 deletions
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134
gtsam/nonlinear/LevenbergMarquardtOptimizer.h
View File

@ -52,76 +52,84 @@ public:
double lambdaLowerBound; ///< The minimum lambda used in LM (default: 0)
VerbosityLM verbosityLM; ///< The verbosity level for Levenberg-Marquardt (default: SILENT), see also NonlinearOptimizerParams::verbosity
double minModelFidelity; ///< Lower bound for the modelFidelity to accept the result of an LM iteration
std::string logFile; ///< an optional CSV log file, with [iteration, time, error, labda]
std::string logFile; ///< an optional CSV log file, with [iteration, time, error, lambda]
bool diagonalDamping; ///< if true, use diagonal of Hessian
bool useFixedLambdaFactor; ///< if true applies constant increase (or decrease) to lambda according to lambdaFactor
double minDiagonal; ///< when using diagonal damping saturates the minimum diagonal entries (default: 1e-6)
double maxDiagonal; ///< when using diagonal damping saturates the maximum diagonal entries (default: 1e32)
LevenbergMarquardtParams()
: lambdaInitial(1e-5),
lambdaFactor(10.0),
lambdaUpperBound(1e5),
lambdaLowerBound(0.0),
verbosityLM(SILENT),
minModelFidelity(1e-3),
: verbosityLM(SILENT),
diagonalDamping(false),
useFixedLambdaFactor(true),
minDiagonal(1e-6),
maxDiagonal(1e32) {}
maxDiagonal(1e32) {
SetLegacyDefaults(this);
}
static void SetLegacyDefaults(LevenbergMarquardtParams* p) {
// Relevant NonlinearOptimizerParams:
p->maxIterations = 100;
p->relativeErrorTol = 1e-5;
p->absoluteErrorTol = 1e-5;
// LM-specific:
p->lambdaInitial = 1e-5;
p->lambdaFactor = 10.0;
p->lambdaUpperBound = 1e5;
p->lambdaLowerBound = 0.0;
p->minModelFidelity = 1e-3;
p->diagonalDamping = false;
p->useFixedLambdaFactor = true;
}
// these do seem to work better for SFM
static void SetCeresDefaults(LevenbergMarquardtParams* p) {
// Relevant NonlinearOptimizerParams:
p->maxIterations = 50;
p->absoluteErrorTol = 0; // No corresponding option in CERES
p->relativeErrorTol = 1e-6; // This is function_tolerance
// LM-specific:
p->lambdaUpperBound = 1e32;
p->lambdaLowerBound = 1e-16;
p->lambdaInitial = 1e-04;
p->lambdaFactor = 2.0;
p->minModelFidelity = 1e-3; // options.min_relative_decrease in CERES
p->diagonalDamping = true;
p->useFixedLambdaFactor = false; // This is important
}
static LevenbergMarquardtParams LegacyDefaults() {
LevenbergMarquardtParams p;
SetLegacyDefaults(&p);
return p;
}
static LevenbergMarquardtParams CeresDefaults() {
LevenbergMarquardtParams p;
// Termination condition, same as options.max_num_iterations
p.maxIterations = 50;
// Termination condition, turn off because no corresponding option in CERES
p.absoluteErrorTol = 0; // Frank thinks this is not tolerance (was 1e-6)
// Termination condition, turn off because no corresponding option in CERES
p.errorTol = 0; // 1e-6;
// Termination condition, same as options.function_tolerance
p.relativeErrorTol = 1e-6; // This is function_tolerance (was 1e-03)
// Change lambda parameters to be the same as Ceres
p.lambdaUpperBound = 1e32;
p.lambdaLowerBound = 1e-16;
p.lambdaInitial = 1e-04;
p.lambdaFactor = 2.0;
p.useFixedLambdaFactor = false; // Luca says this is important
p.diagonalDamping = true;
p.minModelFidelity = 1e-3; // options.min_relative_decrease in CERES
SetCeresDefaults(&p);
return p;
}
virtual ~LevenbergMarquardtParams() {}
virtual void print(const std::string& str = "") const;
inline double getlambdaInitial() const { return lambdaInitial; }
inline double getlambdaFactor() const { return lambdaFactor; }
inline double getlambdaUpperBound() const { return lambdaUpperBound; }
inline double getlambdaLowerBound() const { return lambdaLowerBound; }
inline std::string getVerbosityLM() const {
return verbosityLMTranslator(verbosityLM);
}
inline std::string getLogFile() const { return logFile; }
inline bool getDiagonalDamping() const { return diagonalDamping; }
std::string getVerbosityLM() const { return verbosityLMTranslator(verbosityLM);}
void setVerbosityLM(const std::string& s) { verbosityLM = verbosityLMTranslator(s);}
inline void setlambdaInitial(double value) { lambdaInitial = value; }
inline void setlambdaFactor(double value) { lambdaFactor = value; }
inline void setlambdaUpperBound(double value) { lambdaUpperBound = value; }
inline void setlambdaLowerBound(double value) { lambdaLowerBound = value; }
inline void setVerbosityLM(const std::string& s) {
verbosityLM = verbosityLMTranslator(s);
}
inline void setLogFile(const std::string& s) { logFile = s; }
inline void setDiagonalDamping(bool flag) { diagonalDamping = flag; }
inline void setUseFixedLambdaFactor(bool flag) {
useFixedLambdaFactor = flag;
}
// @deprecated (just use fields)
#ifdef GTSAM_ALLOW_DEPRECATED
bool getDiagonalDamping() const { return diagonalDamping; }
double getlambdaFactor() const { return lambdaFactor; }
double getlambdaInitial() const { return lambdaInitial; }
double getlambdaLowerBound() const { return lambdaLowerBound; }
double getlambdaUpperBound() const { return lambdaUpperBound; }
std::string getLogFile() const { return logFile; }
void setDiagonalDamping(bool flag) { diagonalDamping = flag; }
void setlambdaFactor(double value) { lambdaFactor = value; }
void setlambdaInitial(double value) { lambdaInitial = value; }
void setlambdaLowerBound(double value) { lambdaLowerBound = value; }
void setlambdaUpperBound(double value) { lambdaUpperBound = value; }
void setLogFile(const std::string& s) { logFile = s; }
void setUseFixedLambdaFactor(bool flag) { useFixedLambdaFactor = flag;}
#endif
};
/**
@ -180,12 +188,12 @@ public:
* @param initialValues The initial variable assignments
* @param params The optimization parameters
*/
LevenbergMarquardtOptimizer(const NonlinearFactorGraph& graph,
const Values& initialValues, const LevenbergMarquardtParams& params =
LevenbergMarquardtParams()) :
NonlinearOptimizer(graph), params_(ensureHasOrdering(params, graph)), state_(
graph, initialValues, params_) {
}
LevenbergMarquardtOptimizer(
const NonlinearFactorGraph& graph, const Values& initialValues,
const LevenbergMarquardtParams& params = LevenbergMarquardtParams())
: NonlinearOptimizer(graph),
params_(ensureHasOrdering(params, graph)),
state_(graph, initialValues, params_) {}
/** Standard constructor, requires a nonlinear factor graph, initial
* variable assignments, and optimization parameters. For convenience this
@ -194,9 +202,11 @@ public:
* @param graph The nonlinear factor graph to optimize
* @param initialValues The initial variable assignments
*/
LevenbergMarquardtOptimizer(const NonlinearFactorGraph& graph,
const Values& initialValues, const Ordering& ordering) :
NonlinearOptimizer(graph) {
LevenbergMarquardtOptimizer(
const NonlinearFactorGraph& graph, const Values& initialValues,
const Ordering& ordering,
const LevenbergMarquardtParams& params = LevenbergMarquardtParams())
: NonlinearOptimizer(graph), params_(params) {
params_.ordering = ordering;
state_ = LevenbergMarquardtState(graph, initialValues, params_);
}

25
tests/testNonlinearOptimizer.cpp
View File

@ -187,7 +187,9 @@ TEST( NonlinearOptimizer, Factorization )
ordering.push_back(X(1));
ordering.push_back(X(2));
LevenbergMarquardtOptimizer optimizer(graph, config, ordering);
LevenbergMarquardtParams params;
LevenbergMarquardtParams::SetLegacyDefaults(&params);
LevenbergMarquardtOptimizer optimizer(graph, config, ordering, params);
optimizer.iterate();
Values expected;
@ -260,13 +262,13 @@ TEST_UNSAFE(NonlinearOptimizer, MoreOptimization) {
expectedGradient.insert(2,zero(3));
// Try LM and Dogleg
LevenbergMarquardtParams params;
// params.setVerbosityLM("TRYDELTA");
// params.setVerbosity("TERMINATION");
params.setlambdaUpperBound(1e9);
// params.setRelativeErrorTol(0);
// params.setAbsoluteErrorTol(0);
//params.setlambdaInitial(10);
LevenbergMarquardtParams params = LevenbergMarquardtParams::LegacyDefaults();
// params.setVerbosityLM("TRYDELTA");
// params.setVerbosity("TERMINATION");
params.lambdaUpperBound = 1e9;
// params.relativeErrorTol = 0;
// params.absoluteErrorTol = 0;
//params.lambdaInitial = 10;
{
LevenbergMarquardtOptimizer optimizer(fg, init, params);
@ -290,7 +292,7 @@ TEST_UNSAFE(NonlinearOptimizer, MoreOptimization) {
initBetter.insert(2, Pose2(11,7,M_PI/2));
{
params.setDiagonalDamping(true);
params.diagonalDamping = true;
LevenbergMarquardtOptimizer optimizer(fg, initBetter, params);
// test the diagonal
@ -399,7 +401,7 @@ public:
/// Constructor
IterativeLM(const NonlinearFactorGraph& graph, const Values& initialValues,
const ConjugateGradientParameters &p,
const LevenbergMarquardtParams& params = LevenbergMarquardtParams()) :
const LevenbergMarquardtParams& params = LevenbergMarquardtParams::LegacyDefaults()) :
LevenbergMarquardtOptimizer(graph, initialValues, params), cgParams_(p) {
}
@ -446,8 +448,7 @@ TEST( NonlinearOptimizer, logfile )
// Levenberg-Marquardt
LevenbergMarquardtParams lmParams;
static const string filename("testNonlinearOptimizer.log");
lmParams.setLogFile(filename);
CHECK(lmParams.getLogFile()==filename);
lmParams.logFile = filename;
LevenbergMarquardtOptimizer(fg, c0, lmParams).optimize();
// stringstream expected,actual;