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Tweak to dogleg evaluating error decrease

release/4.3a0
Richard Roberts 2012-04-09 03:02:23 +00:00
parent 2ac1473a84
commit ed91c5c9ff
1 changed files with 6 additions and 4 deletions
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10
gtsam/nonlinear/DoglegOptimizerImpl.h
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@ -5,6 +5,8 @@
*/ */
#pragma once #pragma once
#include <iomanip>
#include <gtsam/linear/GaussianBayesNet.h> #include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianISAM.h> // To get optimize(BayesTree<GaussianConditional>) #include <gtsam/linear/GaussianISAM.h> // To get optimize(BayesTree<GaussianConditional>)
//#include <gtsam/nonlinear/NonlinearFactorGraph.h> //#include <gtsam/nonlinear/NonlinearFactorGraph.h>
@ -180,17 +182,17 @@ typename DoglegOptimizerImpl::IterationResult DoglegOptimizerImpl::Iterate(
const double new_M_error = jfg.error(result.dx_d); const double new_M_error = jfg.error(result.dx_d);
toc(6, "decrease in M"); toc(6, "decrease in M");
if(verbose) cout << "f error: " << f_error << " -> " << result.f_error << endl; if(verbose) cout << setprecision(15) << "f error: " << f_error << " -> " << result.f_error << endl;
if(verbose) cout << "M error: " << M_error << " -> " << new_M_error << endl; if(verbose) cout << setprecision(15) << "M error: " << M_error << " -> " << new_M_error << endl;
tic(7, "adjust Delta"); tic(7, "adjust Delta");
// Compute gain ratio. Here we take advantage of the invariant that the // Compute gain ratio. Here we take advantage of the invariant that the
// Bayes' net error at zero is equal to the nonlinear error // Bayes' net error at zero is equal to the nonlinear error
const double rho = fabs(M_error - new_M_error) < 1e-15 ? const double rho = fabs(f_error - result.f_error) < 1e-15 || fabs(M_error - new_M_error) < 1e-15 ?
0.5 : 0.5 :
(f_error - result.f_error) / (M_error - new_M_error); (f_error - result.f_error) / (M_error - new_M_error);
if(verbose) cout << "rho = " << rho << endl; if(verbose) cout << setprecision(15) << "rho = " << rho << endl;
if(rho >= 0.75) { if(rho >= 0.75) {
// M agrees very well with f, so try to increase lambda // M agrees very well with f, so try to increase lambda