#include <NonlinearOptimizer.h>

Inheritance diagram for gtsam::NonlinearOptimizer:

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Collaboration diagram for gtsam::NonlinearOptimizer:

Public Types
using	shared_ptr = std::shared_ptr< const NonlinearOptimizer >

Public Member Functions
Standard interface
virtual const Values &	optimize ()

const Values &	optimizeSafely ()

double	error () const
	return error in current optimizer state

size_t	iterations () const
	return number of iterations in current optimizer state

const Values &	values () const
	return values in current optimizer state

const NonlinearFactorGraph &	graph () const
	return the graph with nonlinear factors

Advanced interface
virtual	~NonlinearOptimizer ()

virtual VectorValues	solve (const GaussianFactorGraph &gfg, const NonlinearOptimizerParams &params) const

virtual GaussianFactorGraph::shared_ptr	iterate ()=0

Protected Member Functions
void	defaultOptimize ()

virtual const NonlinearOptimizerParams &	_params () const =0

	NonlinearOptimizer (const NonlinearFactorGraph &graph, std::unique_ptr< internal::NonlinearOptimizerState > state)

Protected Attributes
NonlinearFactorGraph	graph_
	The graph with nonlinear factors.

std::unique_ptr< internal::NonlinearOptimizerState >	state_
	PIMPL'd state.

Detailed Description

This is the abstract interface for classes that can optimize for the maximum-likelihood estimate of a NonlinearFactorGraph.

To use a class derived from this interface, construct the class with a NonlinearFactorGraph and an initial Values variable assignment. Next, call the optimize() method which returns the optimized variable assignment.

Simple and compact example:

// One-liner to do full optimization and use the result.

Values result = DoglegOptimizer(graph, initialValues).optimize();

Example exposing more functionality and details:

// Create initial optimizer
DoglegOptimizer optimizer(graph, initialValues);
// Run full optimization until convergence.
Values result = optimizer->optimize();
// The new optimizer has results and statistics
cout << "Converged in " << optimizer.iterations() << " iterations "
        "with final error " << optimizer.error() << endl;

Example of setting parameters before optimization:

// Each derived optimizer type has its own parameters class, which inherits from NonlinearOptimizerParams
DoglegParams params;
params.factorization = DoglegParams::QR;
params.relativeErrorTol = 1e-3;
params.absoluteErrorTol = 1e-3;
// Optimize
Values result = DoglegOptimizer(graph, initialValues, params).optimize();

This interface also exposes an iterate() method, which performs one iteration. The optimize() method simply calls iterate() multiple times, until the error changes less than a threshold. We expose iterate() so that you can easily control what happens between iterations, such as drawing or printing, moving points from behind the camera to in front, etc.

For more flexibility you may override virtual methods in your own derived class.

Member Typedef Documentation

◆ shared_ptr

using gtsam::NonlinearOptimizer::shared_ptr = std::shared_ptr<const NonlinearOptimizer>

A shared pointer to this class

Constructor & Destructor Documentation

◆ ~NonlinearOptimizer()

virtual gtsam::NonlinearOptimizer::~NonlinearOptimizer ( )

virtual

Virtual destructor

◆ NonlinearOptimizer()

gtsam::NonlinearOptimizer::NonlinearOptimizer	(	const NonlinearFactorGraph &	graph,
		std::unique_ptr< internal::NonlinearOptimizerState >	state
	)

protected

Constructor for initial construction of base classes. Takes ownership of state.

Member Function Documentation

◆ defaultOptimize()

void gtsam::NonlinearOptimizer::defaultOptimize ( )

protected

A default implementation of the optimization loop, which calls iterate() until checkConvergence returns true.

◆ iterate()

virtual GaussianFactorGraph::shared_ptr gtsam::NonlinearOptimizer::iterate ( )

pure virtual

Perform a single iteration, returning GaussianFactorGraph corresponding to the linearized factor graph.

Implemented in gtsam::DoglegOptimizer, gtsam::LevenbergMarquardtOptimizer, gtsam::GaussNewtonOptimizer, and gtsam::NonlinearConjugateGradientOptimizer.

◆ optimize()

virtual const Values& gtsam::NonlinearOptimizer::optimize ( )

inlinevirtual

Optimize for the maximum-likelihood estimate, returning a the optimized variable assignments.

This function simply calls iterate() in a loop, checking for convergence with check_convergence(). For fine-grain control over the optimization process, you may call iterate() and check_convergence() yourself, and if needed modify the optimization state between iterations.

Reimplemented in gtsam::NonlinearConjugateGradientOptimizer.

◆ optimizeSafely()

const Values& gtsam::NonlinearOptimizer::optimizeSafely ( )

Optimize, but return empty result if any uncaught exception is thrown Intended for MATLAB. In C++, use above and catch exceptions. No message is printed: it is up to the caller to check the result

Parameters

optimizer a non-linear optimizer

◆ solve()

virtual VectorValues gtsam::NonlinearOptimizer::solve	(	const GaussianFactorGraph &	gfg,
		const NonlinearOptimizerParams &	params
	)		const

virtual

Default function to do linear solve, i.e. optimize a GaussianFactorGraph

The documentation for this class was generated from the following file:

/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/nonlinear/NonlinearOptimizer.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes