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Solved overloading

release/4.3a0
Frank Dellaert 2018-10-02 23:25:27 -04:00
parent 3116fd30b9
commit 566315f47d
1 changed files with 5 additions and 5 deletions
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10
tests/testNonlinearOptimizer.cpp
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@ -20,13 +20,12 @@
#include <gtsam/slam/BetweenFactor.h> #include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h> #include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/Values.h> #include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h> #include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/DoglegOptimizer.h> #include <gtsam/nonlinear/DoglegOptimizer.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h> #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/linear/GaussianFactorGraph.h> #include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/NoiseModel.h> #include <gtsam/linear/NoiseModel.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/geometry/Pose2.h> #include <gtsam/geometry/Pose2.h>
#include <gtsam/base/Matrix.h> #include <gtsam/base/Matrix.h>
@ -394,19 +393,20 @@ class IterativeLM: public LevenbergMarquardtOptimizer {
/// Solver specific parameters /// Solver specific parameters
ConjugateGradientParameters cgParams_; ConjugateGradientParameters cgParams_;
Values initial_;
public: public:
/// Constructor /// Constructor
IterativeLM(const NonlinearFactorGraph& graph, const Values& initialValues, IterativeLM(const NonlinearFactorGraph& graph, const Values& initialValues,
const ConjugateGradientParameters &p, const ConjugateGradientParameters &p,
const LevenbergMarquardtParams& params = LevenbergMarquardtParams::LegacyDefaults()) : const LevenbergMarquardtParams& params = LevenbergMarquardtParams::LegacyDefaults()) :
LevenbergMarquardtOptimizer(graph, initialValues, params), cgParams_(p) { LevenbergMarquardtOptimizer(graph, initialValues, params), cgParams_(p), initial_(initialValues) {
} }
/// Solve that uses conjugate gradient /// Solve that uses conjugate gradient
virtual VectorValues solve(const GaussianFactorGraph &gfg, virtual VectorValues solve(const GaussianFactorGraph &gfg,
const Values& initial, const NonlinearOptimizerParams& params) const { const NonlinearOptimizerParams& params) const {
VectorValues zeros = initial.zeroVectors(); VectorValues zeros = initial_.zeroVectors();
return conjugateGradientDescent(gfg, zeros, cgParams_); return conjugateGradientDescent(gfg, zeros, cgParams_);
} }
}; };