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  /external/ceres-solver/internal/ceres/
trust_region_strategy.cc 14 case DOGLEG:
types.cc 120 CASESTR(DOGLEG);
130 STRENUM(DOGLEG);
solver.cc 191 if (trust_region_strategy_type == DOGLEG) {
trust_region_minimizer_test.cc 266 const TrustRegionStrategyType kStrategy = DOGLEG;
solver_impl.cc 773 if (options->trust_region_strategy_type == DOGLEG) {
776 *error = "DOGLEG only supports exact factorization based linear "
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solver_impl_test.cc 562 options.trust_region_strategy_type = DOGLEG;
  /external/ceres-solver/include/ceres/
types.h 163 // Powell's dogleg algorithm interpolates between the Cauchy point
177 DOGLEG
180 // Ceres supports two different dogleg strategies.
181 // The "traditional" dogleg method by Powell and the
188 // The traditional approach constructs a dogleg path
194 // constrained to the subspace spanned by the dogleg path.
  /external/ceres-solver/docs/
solving.tex 56 There are a number of different ways of solving this problem, each giving rise to a different concrete trust-region algorithm. Currently Ceres, implements two trust-region algorithms - Levenberg-Marquardt and Dogleg.
89 \subsection{Dogleg}
90 \label{sec:dogleg}
99 ourselves to moving along the direction of the gradient. Dogleg methods finds a vector $\Delta x$ defined by $\Delta
106 and finds the point farthest along this line shaped like a dogleg
115 The key advantage of the Dogleg over Levenberg Marquardt is that if the step computation for a particular choice of $\mu$ does not result in sufficient decrease in the value of the objective function, Levenberg-Marquardt solves the linear approximation from scratch with a smaller value of $\mu$. Dogleg on the other hand, only needs to compute the interpolation between the Gauss-Newton and the Cauchy vectors, as neither of them depend on the value of $\mu$.
117 The Dogleg method can only be used with the exact factorization based linear solvers.
437 and \texttt{DOGLEG} are the two valid choices.
439 \item{\texttt{dogleg\_type}} (\texttt{TRADITIONAL\_DOGLEG}) Cere
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