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remove a custom constructor for LMParams, update matlab interface and tests accordingly

release/4.3a0
Yong-Dian Jian 2012-07-05 15:08:07 +00:00
parent d89a9e086c
commit e437084349
4 changed files with 4 additions and 9 deletions
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1
gtsam.h
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@ -1011,7 +1011,6 @@ class Marginals {
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
class LevenbergMarquardtParams {
LevenbergMarquardtParams();
LevenbergMarquardtParams(double initial, double factor, double bound, size_t verbose);
void print(string s) const;
double getMaxIterations() const;

8
gtsam/nonlinear/LevenbergMarquardtOptimizer.h
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@ -46,13 +46,9 @@ public:
double lambdaUpperBound; ///< The maximum lambda to try before assuming the optimization has failed (default: 1e5)
VerbosityLM verbosityLM; ///< The verbosity level for Levenberg-Marquardt (default: SILENT), see also NonlinearOptimizerParams::verbosity
LevenbergMarquardtParams() :
lambdaInitial(1e-5), lambdaFactor(10.0), lambdaUpperBound(1e5), verbosityLM(SILENT) {}
LevenbergMarquardtParams(double initial, double factor, double bound, size_t verbose) :
lambdaInitial(initial), lambdaFactor(factor), lambdaUpperBound(bound), verbosityLM(VerbosityLM(verbose)) {}
LevenbergMarquardtParams() : lambdaInitial(1e-5), lambdaFactor(10.0), lambdaUpperBound(1e5), verbosityLM(SILENT) {}
virtual ~LevenbergMarquardtParams() {}
virtual void print(const std::string& str = "") const;
inline double getlambdaInitial() const { return lambdaInitial; }

2
matlab/examples/SFMExample.m
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@ -63,7 +63,7 @@ initialEstimate.print(sprintf('\nInitial estimate:\n '));
%% Fine grain optimization, allowing user to iterate step by step
parameters = gtsamLevenbergMarquardtParams(1e-5, 1e-5, 0, 0);
parameters = gtsamLevenbergMarquardtParams;
parameters.setlambdaInitial(1.0);
parameters.setVerbosityLM('trylambda');

2
matlab/tests/testSFMExample.m
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@ -48,7 +48,7 @@ for j=1:size(truth.points,2)
end
%% Optimization
parameters = gtsamLevenbergMarquardtParams(1e-5, 1e-5, 0, 0);
parameters = gtsamLevenbergMarquardtParams;
optimizer = graph.optimizer(initialEstimate, parameters);
for i=1:5
optimizer.iterate();