#include <GaussianFactorGraph.h>

Inheritance diagram for gtsam::GaussianFactorGraph:

Collaboration diagram for gtsam::GaussianFactorGraph:

Public Types
typedef GaussianFactorGraph	This
	Typedef to this class.

typedef FactorGraph< GaussianFactor >	Base
	Typedef to base factor graph type.

typedef EliminateableFactorGraph< This >	BaseEliminateable
	Typedef to base elimination class.

typedef std::shared_ptr< This >	shared_ptr
	shared_ptr to this class

typedef KeySet	Keys

typedef GaussianFactor	FactorType
	factor type

typedef std::shared_ptr< GaussianFactor >	sharedFactor
	Shared pointer to a factor.

typedef sharedFactor	value_type

typedef FastVector< sharedFactor >::iterator	iterator

typedef FastVector< sharedFactor >::const_iterator	const_iterator

typedef EliminationTraits< FactorGraphType >	EliminationTraitsType
	Typedef to the specific EliminationTraits for this graph.

typedef EliminationTraitsType::ConditionalType	ConditionalType
	Conditional type stored in the Bayes net produced by elimination.

typedef EliminationTraitsType::BayesNetType	BayesNetType
	Bayes net type produced by sequential elimination.

typedef EliminationTraitsType::EliminationTreeType	EliminationTreeType
	Elimination tree type that can do sequential elimination of this graph.

typedef EliminationTraitsType::BayesTreeType	BayesTreeType
	Bayes tree type produced by multifrontal elimination.

typedef EliminationTraitsType::JunctionTreeType	JunctionTreeType
	Junction tree type that can do multifrontal elimination of this graph.

typedef std::pair< std::shared_ptr< ConditionalType >, std::shared_ptr< _FactorType > >	EliminationResult

typedef std::function< EliminationResult(const FactorGraphType &, const Ordering &)>	Eliminate
	The function type that does a single dense elimination step on a subgraph.

typedef std::optional< std::reference_wrapper< const VariableIndex > >	OptionalVariableIndex

typedef std::optional< Ordering::OrderingType >	OptionalOrderingType
	Typedef for an optional ordering type.

Public Member Functions
void	add (const GaussianFactor &factor)

void	add (const sharedFactor &factor)

void	add (const Vector &b)

void	add (Key key1, const Matrix &A1, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

void	add (Key key1, const Matrix &A1, Key key2, const Matrix &A2, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

void	add (Key key1, const Matrix &A1, Key key2, const Matrix &A2, Key key3, const Matrix &A3, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

template<class TERMS >
void	add (const TERMS &terms, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

Keys	keys () const

std::map< Key, size_t >	getKeyDimMap () const

double	error (const VectorValues &x) const

double	probPrime (const VectorValues &c) const

virtual GaussianFactorGraph	clone () const

virtual GaussianFactorGraph::shared_ptr	cloneToPtr () const

GaussianFactorGraph	negate () const

std::shared_ptr< BayesNetType >	eliminateSequential (OptionalOrderingType orderingType={}, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	eliminateSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	eliminateMultifrontal (OptionalOrderingType orderingType={}, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	eliminateMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > >	eliminatePartialSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesNetType >, std::shared_ptr< FactorGraphType > >	eliminatePartialSequential (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > >	eliminatePartialMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::pair< std::shared_ptr< BayesTreeType >, std::shared_ptr< FactorGraphType > >	eliminatePartialMultifrontal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const Ordering &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const Ordering &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesNetType >	marginalMultifrontalBayesNet (const KeyVector &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const Ordering &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const Ordering &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< BayesTreeType >	marginalMultifrontalBayesTree (const KeyVector &variables, const Ordering &marginalizedVariableOrdering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

std::shared_ptr< FactorGraphType >	marginal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex={}) const

Constructors
	GaussianFactorGraph ()

	GaussianFactorGraph (std::initializer_list< sharedFactor > factors)

template<typename ITERATOR >
	GaussianFactorGraph (ITERATOR firstFactor, ITERATOR lastFactor)

template<class CONTAINER >
	GaussianFactorGraph (const CONTAINER &factors)

template<class DERIVEDFACTOR >
	GaussianFactorGraph (const FactorGraph< DERIVEDFACTOR > &graph)

Testable
bool	equals (const This &fg, double tol=1e-9) const

Linear Algebra
std::vector< std::tuple< int, int, double > >	sparseJacobian (const Ordering &ordering, size_t &nrows, size_t &ncols) const

std::vector< std::tuple< int, int, double > >	sparseJacobian () const

Matrix	sparseJacobian_ () const

Matrix	augmentedJacobian (const Ordering &ordering) const

Matrix	augmentedJacobian () const

std::pair< Matrix, Vector >	jacobian (const Ordering &ordering) const

std::pair< Matrix, Vector >	jacobian () const

Matrix	augmentedHessian (const Ordering &ordering) const

Matrix	augmentedHessian () const

std::pair< Matrix, Vector >	hessian (const Ordering &ordering) const

std::pair< Matrix, Vector >	hessian () const

virtual VectorValues	hessianDiagonal () const

virtual std::map< Key, Matrix >	hessianBlockDiagonal () const

VectorValues	optimize (const Eliminate &function=EliminationTraitsType::DefaultEliminate) const

VectorValues	optimize (const Ordering &, const Eliminate &function=EliminationTraitsType::DefaultEliminate) const

VectorValues	optimizeDensely () const

VectorValues	gradient (const VectorValues &x0) const

virtual VectorValues	gradientAtZero () const

VectorValues	optimizeGradientSearch () const

VectorValues	transposeMultiply (const Errors &e) const

void	transposeMultiplyAdd (double alpha, const Errors &e, VectorValues &x) const

Errors	gaussianErrors (const VectorValues &x) const

Errors	operator* (const VectorValues &x) const

void	multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const

void	multiplyInPlace (const VectorValues &x, Errors &e) const

void	multiplyInPlace (const VectorValues &x, const Errors::iterator &e) const

void	printErrors (const VectorValues &x, const std::string &str="GaussianFactorGraph: ", const KeyFormatter &keyFormatter=DefaultKeyFormatter, const std::function< bool(const Factor , double, size_t)> &printCondition=[](const Factor , double, size_t) { return true;}) const

Adding Single Factors
void	reserve (size_t size)

IsDerived< DERIVEDFACTOR >	push_back (std::shared_ptr< DERIVEDFACTOR > factor)
	Add a factor directly using a shared_ptr.

IsDerived< DERIVEDFACTOR >	push_back (const DERIVEDFACTOR &factor)

IsDerived< DERIVEDFACTOR >	emplace_shared (Args &&... args)
	Emplace a shared pointer to factor of given type.

IsDerived< DERIVEDFACTOR >	add (std::shared_ptr< DERIVEDFACTOR > factor)
	`add` is a synonym for push_back.

Adding via iterators
HasDerivedElementType< ITERATOR >	push_back (ITERATOR firstFactor, ITERATOR lastFactor)

HasDerivedValueType< ITERATOR >	push_back (ITERATOR firstFactor, ITERATOR lastFactor)
	Push back many factors with an iterator (factors are copied)

Adding via container
HasDerivedElementType< CONTAINER >	push_back (const CONTAINER &container)

HasDerivedValueType< CONTAINER >	push_back (const CONTAINER &container)
	Push back non-pointer objects in a container (factors are copied).

void	add (const FACTOR_OR_CONTAINER &factorOrContainer)

Specialized versions
std::enable_if< std::is_base_of< This, typename CLIQUE::FactorGraphType >::value >::type	push_back (const BayesTree< CLIQUE > &bayesTree)

FactorIndices	add_factors (const CONTAINER &factors, bool useEmptySlots=false)

Testable
virtual void	print (const std::string &s="FactorGraph", const KeyFormatter &formatter=DefaultKeyFormatter) const
	Print out graph to std::cout, with optional key formatter.

bool	equals (const This &fg, double tol=1e-9) const
	Check equality up to tolerance.

Standard Interface
size_t	size () const

bool	empty () const

const sharedFactor	at (size_t i) const

sharedFactor &	at (size_t i)

const sharedFactor	operator[] (size_t i) const

sharedFactor &	operator[] (size_t i)

const_iterator	begin () const

const_iterator	end () const

sharedFactor	front () const

sharedFactor	back () const

double	error (const HybridValues &values) const

Modifying Factor Graphs (imperative, discouraged)
iterator	begin ()

iterator	end ()

virtual void	resize (size_t size)

void	remove (size_t i)

void	replace (size_t index, sharedFactor factor)

iterator	erase (iterator item)

iterator	erase (iterator first, iterator last)

Graph Display
void	dot (std::ostream &os, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
	Output to graphviz format, stream version.

std::string	dot (const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
	Output to graphviz format string.

void	saveGraph (const std::string &filename, const KeyFormatter &keyFormatter=DefaultKeyFormatter, const DotWriter &writer=DotWriter()) const
	output to file with graphviz format.

Advanced Interface
size_t	nrFactors () const

KeyVector	keyVector () const

bool	exists (size_t idx) const

Protected Member Functions
bool	isEqual (const FactorGraph &other) const
	Check exact equality of the factor pointers. Useful for derived ==.

Protected Attributes
FastVector< sharedFactor >	factors_

Friends
bool	operator== (const GaussianFactorGraph &lhs, const GaussianFactorGraph &rhs)
	Check exact equality.

Detailed Description

A Linear Factor Graph is a factor graph where all factors are Gaussian, i.e. Factor == GaussianFactor VectorValues = A values structure of vectors Most of the time, linear factor graphs arise by linearizing a non-linear factor graph.

Member Typedef Documentation

◆ EliminationResult

typedef std::pair<std::shared_ptr<ConditionalType>, std::shared_ptr<_FactorType> > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::EliminationResult

inherited

The pair of conditional and remaining factor produced by a single dense elimination step on a subgraph.

◆ Keys

typedef KeySet gtsam::GaussianFactorGraph::Keys

Return the set of variables involved in the factors (computes a set union).

◆ OptionalVariableIndex

typedef std::optional<std::reference_wrapper<const VariableIndex> > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::OptionalVariableIndex

inherited

Typedef for an optional variable index as an argument to elimination functions It is an optional to a constant reference

Constructor & Destructor Documentation

◆ GaussianFactorGraph() [1/5]

gtsam::GaussianFactorGraph::GaussianFactorGraph ( )

inline

Default constructor

◆ GaussianFactorGraph() [2/5]

gtsam::GaussianFactorGraph::GaussianFactorGraph ( std::initializer_list< sharedFactor > factors )

inline

Construct from an initializer lists of GaussianFactor shared pointers. Example: GaussianFactorGraph graph = { factor1, factor2, factor3 };

◆ GaussianFactorGraph() [3/5]

template<typename ITERATOR >

gtsam::GaussianFactorGraph::GaussianFactorGraph	(	ITERATOR	firstFactor,
		ITERATOR	lastFactor
	)

inline

Construct from iterator over factors

◆ GaussianFactorGraph() [4/5]

template<class CONTAINER >

gtsam::GaussianFactorGraph::GaussianFactorGraph ( const CONTAINER & factors )

inlineexplicit

Construct from container of factors (shared_ptr or plain objects)

◆ GaussianFactorGraph() [5/5]

template<class DERIVEDFACTOR >

gtsam::GaussianFactorGraph::GaussianFactorGraph ( const FactorGraph< DERIVEDFACTOR > & graph )

inline

Implicit copy/downcast constructor to override explicit template container constructor

Member Function Documentation

◆ add() [1/8]

void gtsam::GaussianFactorGraph::add ( const GaussianFactor & factor )

inline

Add a factor by value - makes a copy

◆ add() [2/8]

void gtsam::GaussianFactorGraph::add ( const sharedFactor & factor )

inline

Add a factor by pointer - stores pointer without copying the factor

◆ add() [3/8]

void gtsam::GaussianFactorGraph::add ( const Vector & b )

inline

Add a null factor

◆ add() [4/8]

void gtsam::GaussianFactorGraph::add	(	Key	key1,
		const Matrix &	A1,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

inline

Add a unary factor

◆ add() [5/8]

void gtsam::GaussianFactorGraph::add	(	Key	key1,
		const Matrix &	A1,
		Key	key2,
		const Matrix &	A2,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

inline

Add a binary factor

◆ add() [6/8]

void gtsam::GaussianFactorGraph::add	(	Key	key1,
		const Matrix &	A1,
		Key	key2,
		const Matrix &	A2,
		Key	key3,
		const Matrix &	A3,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

inline

Add a ternary factor

◆ add() [7/8]

template<class TERMS >

void gtsam::GaussianFactorGraph::add	(	const TERMS &	terms,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

inline

Add an n-ary factor

◆ add() [8/8]

void gtsam::FactorGraph< GaussianFactor >::add ( const FACTOR_OR_CONTAINER & factorOrContainer )

inlineinherited

Add a factor or container of factors, including STL collections, BayesTrees, etc.

◆ add_factors()

FactorIndices gtsam::FactorGraph< GaussianFactor >::add_factors	(	const CONTAINER &	factors,
		bool	useEmptySlots = `false`
	)

inherited

Add new factors to a factor graph and returns a list of new factor indices, optionally finding and reusing empty factor slots.

◆ at() [1/2]

const sharedFactor gtsam::FactorGraph< GaussianFactor >::at ( size_t i ) const

inlineinherited

Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).

◆ at() [2/2]

sharedFactor& gtsam::FactorGraph< GaussianFactor >::at ( size_t i )

inlineinherited

Get a specific factor by index (this checks array bounds and may throw an exception, as opposed to operator[] which does not).

◆ augmentedHessian() [1/2]

Matrix gtsam::GaussianFactorGraph::augmentedHessian ( const Ordering & ordering ) const

Return a dense $ \Lambda \in \mathbb{R}^{n+1 \times n+1} $ Hessian matrix, augmented with the information vector $ \eta $. The augmented Hessian is

\[ \left[ \begin{array}{ccc} \Lambda & \eta \\ \eta^T & c \end{array} \right] \]

and the negative log-likelihood is $ \frac{1}{2} x^T \Lambda x + \eta^T x + c $.

◆ augmentedHessian() [2/2]

Matrix gtsam::GaussianFactorGraph::augmentedHessian ( ) const

Return a dense $ \Lambda \in \mathbb{R}^{n+1 \times n+1} $ Hessian matrix, augmented with the information vector $ \eta $. The augmented Hessian is

\[ \left[ \begin{array}{ccc} \Lambda & \eta \\ \eta^T & c \end{array} \right] \]

and the negative log-likelihood is $ \frac{1}{2} x^T \Lambda x + \eta^T x + c $.

◆ augmentedJacobian() [1/2]

Matrix gtsam::GaussianFactorGraph::augmentedJacobian ( const Ordering & ordering ) const

Return a dense $ [ \;A\;b\; ] \in \mathbb{R}^{m \times n+1} $ Jacobian matrix, augmented with b with the noise models baked into A and b. The negative log-likelihood is $ \frac{1}{2} \Vert Ax-b \Vert^2 $. See also GaussianFactorGraph::jacobian and GaussianFactorGraph::sparseJacobian.

◆ augmentedJacobian() [2/2]

Matrix gtsam::GaussianFactorGraph::augmentedJacobian ( ) const

Return a dense $ [ \;A\;b\; ] \in \mathbb{R}^{m \times n+1} $ Jacobian matrix, augmented with b with the noise models baked into A and b. The negative log-likelihood is $ \frac{1}{2} \Vert Ax-b \Vert^2 $. See also GaussianFactorGraph::jacobian and GaussianFactorGraph::sparseJacobian.

◆ back()

sharedFactor gtsam::FactorGraph< GaussianFactor >::back ( ) const

inlineinherited

Get the last factor

◆ begin() [1/2]

const_iterator gtsam::FactorGraph< GaussianFactor >::begin ( ) const

inlineinherited

Iterator to beginning of factors.

◆ begin() [2/2]

iterator gtsam::FactorGraph< GaussianFactor >::begin ( )

inlineinherited

non-const STL-style begin()

◆ clone()

virtual GaussianFactorGraph gtsam::GaussianFactorGraph::clone ( ) const

virtual

Clone() performs a deep-copy of the graph, including all of the factors. Cloning preserves null factors so indices for the original graph are still valid for the cloned graph.

◆ cloneToPtr()

virtual GaussianFactorGraph::shared_ptr gtsam::GaussianFactorGraph::cloneToPtr ( ) const

virtual

CloneToPtr() performs a simple assignment to a new graph and returns it. There is no preservation of null factors!

◆ eliminateMultifrontal() [1/2]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminateMultifrontal	(	OptionalOrderingType	orderingType = `{}`,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, depending on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)

Example - Full Cholesky elimination in COLAMD order:

std::shared_ptr<GaussianBayesTree> result = graph.eliminateMultifrontal(EliminateCholesky);

Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:

VariableIndex varIndex(graph); // Build variable index
Data data = otherFunctionUsingVariableIndex(graph, varIndex); // Other code that uses variable index
std::shared_ptr<GaussianBayesTree> result = graph.eliminateMultifrontal(EliminateQR, {}, varIndex);

◆ eliminateMultifrontal() [2/2]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminateMultifrontal	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of all variables to produce a Bayes tree. If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, depending on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)

Example - Full QR elimination in specified order:

std::shared_ptr<GaussianBayesTree> result = graph.eliminateMultifrontal(EliminateQR, myOrdering);

◆ eliminatePartialMultifrontal() [1/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType >, std::shared_ptr< GaussianFactorGraph > > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminatePartialMultifrontal	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of some variables, in ordering provided, to produce a Bayes tree and a remaining factor graph. This computes the factorization $ p(X) = p(A|B) p(B) $, where $ A = $ variables, $ X $ is all the variables in the factor graph, and $ B = X\backslash A $.

◆ eliminatePartialMultifrontal() [2/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType >, std::shared_ptr< GaussianFactorGraph > > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminatePartialMultifrontal	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do multifrontal elimination of the given variables in an ordering computed by COLAMD to produce a Bayes tree and a remaining factor graph. This computes the factorization $ p(X) = p(A|B) p(B) $, where $ A = $ variables, $ X $ is all the variables in the factor graph, and $ B = X\backslash A $.

◆ eliminatePartialSequential() [1/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType >, std::shared_ptr< GaussianFactorGraph > > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminatePartialSequential	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of some variables, in ordering provided, to produce a Bayes net and a remaining factor graph. This computes the factorization $ p(X) = p(A|B) p(B) $, where $ A = $ variables, $ X $ is all the variables in the factor graph, and $ B = X\backslash A $.

◆ eliminatePartialSequential() [2/2]

std::pair< std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType >, std::shared_ptr< GaussianFactorGraph > > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminatePartialSequential	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of the given variables in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph. This computes the factorization $ p(X) = p(A|B) p(B) $, where $ A = $ variables, $ X $ is all the variables in the factor graph, and $ B = X\backslash A $.

◆ eliminateSequential() [1/2]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminateSequential	(	OptionalOrderingType	orderingType = `{}`,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of all variables to produce a Bayes net. If an ordering is not provided, the ordering provided by COLAMD will be used.

Example - Full Cholesky elimination in COLAMD order:

std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(EliminateCholesky);

Example - METIS ordering for elimination

std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(OrderingType::METIS);

Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:

VariableIndex varIndex(graph); // Build variable index
Data data = otherFunctionUsingVariableIndex(graph, varIndex); // Other code that uses variable index
std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(EliminateQR, varIndex, std::nullopt);

◆ eliminateSequential() [2/2]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::eliminateSequential	(	const Ordering &	ordering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Do sequential elimination of all variables to produce a Bayes net.

Example - Full QR elimination in specified order:

std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(myOrdering, EliminateQR);

Example - Reusing an existing VariableIndex to improve performance:

VariableIndex varIndex(graph); // Build variable index
Data data = otherFunctionUsingVariableIndex(graph, varIndex); // Other code that uses variable index
std::shared_ptr<GaussianBayesNet> result = graph.eliminateSequential(myOrdering, EliminateQR, varIndex, std::nullopt);

◆ empty()

bool gtsam::FactorGraph< GaussianFactor >::empty ( ) const

inlineinherited

Check if the graph is empty (null factors set by remove() will cause this to return false).

◆ end() [1/2]

const_iterator gtsam::FactorGraph< GaussianFactor >::end ( ) const

inlineinherited

Iterator to end of factors.

◆ end() [2/2]

iterator gtsam::FactorGraph< GaussianFactor >::end ( )

inlineinherited

non-const STL-style end()

◆ erase() [1/2]

iterator gtsam::FactorGraph< GaussianFactor >::erase ( iterator item )

inlineinherited

Erase factor and rearrange other factors to take up the empty space

◆ erase() [2/2]

iterator gtsam::FactorGraph< GaussianFactor >::erase	(	iterator	first,
		iterator	last
	)

inlineinherited

Erase factors and rearrange other factors to take up the empty space

◆ error() [1/2]

double gtsam::GaussianFactorGraph::error ( const VectorValues & x ) const

unnormalized error

◆ error() [2/2]

double gtsam::FactorGraph< GaussianFactor >::error ( const HybridValues & values ) const

inherited

Add error for all factors.

◆ exists()

bool gtsam::FactorGraph< GaussianFactor >::exists ( size_t idx ) const

inlineinherited

MATLAB interface utility: Checks whether a factor index idx exists in the graph and is a live pointer

◆ front()

sharedFactor gtsam::FactorGraph< GaussianFactor >::front ( ) const

inlineinherited

Get the first factor

◆ gaussianErrors()

Errors gtsam::GaussianFactorGraph::gaussianErrors ( const VectorValues & x ) const

return A*x-b

◆ gradient()

VectorValues gtsam::GaussianFactorGraph::gradient ( const VectorValues & x0 ) const

Compute the gradient of the energy function, $ \nabla_{x=x_0} \left\Vert \Sigma^{-1} A x - b \right\Vert^2 $, centered around $ x = x_0 $. The gradient is $ A^T(Ax-b) $.

Parameters

fg	The Jacobian factor graph $(A,b)$
x0	The center about which to compute the gradient

Returns: The gradient as a VectorValues

◆ gradientAtZero()

virtual VectorValues gtsam::GaussianFactorGraph::gradientAtZero ( ) const

virtual

Compute the gradient of the energy function, $ \nabla_{x=0} \left\Vert \Sigma^{-1} A x - b \right\Vert^2 $, centered around zero. The gradient is $ A^T(Ax-b) $.

Parameters

fg	The Jacobian factor graph $(A,b)$
[output]	g A VectorValues to store the gradient, which must be preallocated, see allocateVectorValues

Returns: The gradient as a VectorValues

◆ hessian() [1/2]

std::pair<Matrix,Vector> gtsam::GaussianFactorGraph::hessian ( const Ordering & ordering ) const

Return the dense Hessian $ \Lambda $ and information vector $ \eta $, with the noise models baked in. The negative log-likelihood is {1}{2} x^T x + ^T x + c. See also GaussianFactorGraph::augmentedHessian.

◆ hessian() [2/2]

std::pair<Matrix,Vector> gtsam::GaussianFactorGraph::hessian ( ) const

Return the dense Hessian $ \Lambda $ and information vector $ \eta $, with the noise models baked in. The negative log-likelihood is {1}{2} x^T x + ^T x + c. See also GaussianFactorGraph::augmentedHessian.

◆ hessianBlockDiagonal()

virtual std::map<Key,Matrix> gtsam::GaussianFactorGraph::hessianBlockDiagonal ( ) const

virtual

Return the block diagonal of the Hessian for this factor

◆ hessianDiagonal()

virtual VectorValues gtsam::GaussianFactorGraph::hessianDiagonal ( ) const

virtual

Return only the diagonal of the Hessian A'*A, as a VectorValues

◆ jacobian() [1/2]

std::pair<Matrix,Vector> gtsam::GaussianFactorGraph::jacobian ( const Ordering & ordering ) const

Return the dense Jacobian $ A $ and right-hand-side $ b $, with the noise models baked into A and b. The negative log-likelihood is $ \frac{1}{2} \Vert Ax-b \Vert^2 $. See also GaussianFactorGraph::augmentedJacobian and GaussianFactorGraph::sparseJacobian.

◆ jacobian() [2/2]

std::pair<Matrix,Vector> gtsam::GaussianFactorGraph::jacobian ( ) const

Return the dense Jacobian $ A $ and right-hand-side $ b $, with the noise models baked into A and b. The negative log-likelihood is $ \frac{1}{2} \Vert Ax-b \Vert^2 $. See also GaussianFactorGraph::augmentedJacobian and GaussianFactorGraph::sparseJacobian.

◆ keyVector()

KeyVector gtsam::FactorGraph< GaussianFactor >::keyVector ( ) const

inherited

Potentially slow function to return all keys involved, sorted, as a vector

◆ marginal()

std::shared_ptr< GaussianFactorGraph > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginal	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal factor graph of the requested variables.

◆ marginalMultifrontalBayesNet() [1/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesNet	(	const Ordering &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net. Uses COLAMD marginalization ordering by default

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesNet() [2/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesNet	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net. Uses COLAMD marginalization ordering by default

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesNet() [3/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesNet	(	const Ordering &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net.

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesNet() [4/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesNetType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesNet	(	const KeyVector &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes net.

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function.
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [1/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesTree	(	const Ordering &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree. Uses COLAMD marginalization order by default

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [2/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesTree	(	const KeyVector &	variables,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree. Uses COLAMD marginalization order by default

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [3/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesTree	(	const Ordering &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree.

Parameters

variables	Determines the ordered variables whose marginal to compute, will be ordered in the returned BayesNet as specified.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ marginalMultifrontalBayesTree() [4/4]

std::shared_ptr< typename EliminateableFactorGraph< GaussianFactorGraph >::BayesTreeType > gtsam::EliminateableFactorGraph< GaussianFactorGraph >::marginalMultifrontalBayesTree	(	const KeyVector &	variables,
		const Ordering &	marginalizedVariableOrdering,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`,
		OptionalVariableIndex	variableIndex = `{}`
	)		const

inherited

Compute the marginal of the requested variables and return the result as a Bayes tree.

Parameters

variables	Determines the variables whose marginal to compute, will be ordered using COLAMD; use `Ordering(variables)` to specify the variable ordering.
marginalizedVariableOrdering	Ordering for the variables being marginalized out, i.e. all variables not in `variables`.
function	Optional dense elimination function..
variableIndex	Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed.

◆ multiplyHessianAdd()

void gtsam::GaussianFactorGraph::multiplyHessianAdd	(	double	alpha,
		const VectorValues &	x,
		VectorValues &	y
	)		const

y += alpha*A'A*x

◆ multiplyInPlace() [1/2]

void gtsam::GaussianFactorGraph::multiplyInPlace	(	const VectorValues &	x,
		Errors &	e
	)		const

In-place version e <- A*x that overwrites e.

◆ multiplyInPlace() [2/2]

void gtsam::GaussianFactorGraph::multiplyInPlace	(	const VectorValues &	x,
		const Errors::iterator &	e
	)		const

In-place version e <- A*x that takes an iterator.

◆ negate()

GaussianFactorGraph gtsam::GaussianFactorGraph::negate ( ) const

Returns the negation of all factors in this graph - corresponds to antifactors. Will convert all factors to HessianFactors due to negation of information. Cloning preserves null factors so indices for the original graph are still valid for the cloned graph.

◆ nrFactors()

size_t gtsam::FactorGraph< GaussianFactor >::nrFactors ( ) const

inherited

return the number of non-null factors

◆ operator*()

Errors gtsam::GaussianFactorGraph::operator* ( const VectorValues & x ) const

return A*x

◆ operator[]() [1/2]

const sharedFactor gtsam::FactorGraph< GaussianFactor >::operator[] ( size_t i ) const

inlineinherited

Get a specific factor by index (this does not check array bounds, as opposed to at() which does).

◆ operator[]() [2/2]

sharedFactor& gtsam::FactorGraph< GaussianFactor >::operator[] ( size_t i )

inlineinherited

Get a specific factor by index (this does not check array bounds, as opposed to at() which does).

◆ optimize() [1/2]

VectorValues gtsam::GaussianFactorGraph::optimize ( const Eliminate & function = EliminationTraitsType::DefaultEliminate ) const

Solve the factor graph by performing multifrontal variable elimination in COLAMD order using the dense elimination function specified in function (default EliminatePreferCholesky), followed by back-substitution in the Bayes tree resulting from elimination. Is equivalent to calling graph.eliminateMultifrontal()->optimize().

◆ optimize() [2/2]

VectorValues gtsam::GaussianFactorGraph::optimize	(	const Ordering &	,
		const Eliminate &	function = `EliminationTraitsType::DefaultEliminate`
	)		const

Solve the factor graph by performing multifrontal variable elimination in COLAMD order using the dense elimination function specified in function (default EliminatePreferCholesky), followed by back-substitution in the Bayes tree resulting from elimination. Is equivalent to calling graph.eliminateMultifrontal()->optimize().

◆ optimizeDensely()

VectorValues gtsam::GaussianFactorGraph::optimizeDensely ( ) const

Optimize using Eigen's dense Cholesky factorization

◆ optimizeGradientSearch()

VectorValues gtsam::GaussianFactorGraph::optimizeGradientSearch ( ) const

Optimize along the gradient direction, with a closed-form computation to perform the line search. The gradient is computed about $ \delta x=0 $.

This function returns $ \delta x $ that minimizes a reparametrized problem. The error function of a GaussianBayesNet is

\[ f(\delta x) = \frac{1}{2} |R \delta x - d|^2 = \frac{1}{2}d^T d - d^T R \delta x + \frac{1}{2} \delta x^T R^T R \delta x \]

with gradient and Hessian

\[ g(\delta x) = R^T(R\delta x - d), \qquad G(\delta x) = R^T R. \]

This function performs the line search in the direction of the gradient evaluated at $ g = g(\delta x = 0) $ with step size $ \alpha $ that minimizes $ f(\delta x = \alpha g) $:

\[ f(\alpha) = \frac{1}{2} d^T d + g^T \delta x + \frac{1}{2} \alpha^2 g^T G g \]

Optimizing by setting the derivative to zero yields $ \hat \alpha = (-g^T g) / (g^T G g) $. For efficiency, this function evaluates the denominator without computing the Hessian $ G $, returning

\[ \delta x = \hat\alpha g = \frac{-g^T g}{(R g)^T(R g)} \]

◆ probPrime()

double gtsam::GaussianFactorGraph::probPrime ( const VectorValues & c ) const

Unnormalized probability. O(n)

◆ push_back() [1/4]

IsDerived<DERIVEDFACTOR> gtsam::FactorGraph< GaussianFactor >::push_back ( const DERIVEDFACTOR & factor )

inlineinherited

Add a factor by value, will be copy-constructed (use push_back with a shared_ptr to avoid the copy).

◆ push_back() [2/4]

HasDerivedElementType<ITERATOR> gtsam::FactorGraph< GaussianFactor >::push_back	(	ITERATOR	firstFactor,
		ITERATOR	lastFactor
	)

inlineinherited

Push back many factors with an iterator over shared_ptr (factors are not copied)

◆ push_back() [3/4]

HasDerivedElementType<CONTAINER> gtsam::FactorGraph< GaussianFactor >::push_back ( const CONTAINER & container )

inlineinherited

Push back many factors as shared_ptr's in a container (factors are not copied)

◆ push_back() [4/4]

std::enable_if< std::is_base_of<This, typename CLIQUE::FactorGraphType>::value>::type gtsam::FactorGraph< GaussianFactor >::push_back ( const BayesTree< CLIQUE > & bayesTree )

inlineinherited

Push back a BayesTree as a collection of factors. NOTE: This should be hidden in derived classes in favor of a type-specialized version that calls this templated function.

◆ remove()

void gtsam::FactorGraph< GaussianFactor >::remove ( size_t i )

inlineinherited

delete factor without re-arranging indexes by inserting a nullptr pointer

◆ replace()

void gtsam::FactorGraph< GaussianFactor >::replace	(	size_t	index,
		sharedFactor	factor
	)

inlineinherited

replace a factor by index

◆ reserve()

void gtsam::FactorGraph< GaussianFactor >::reserve ( size_t size )

inlineinherited

Reserve space for the specified number of factors if you know in advance how many there will be (works like FastVector::reserve).

◆ resize()

virtual void gtsam::FactorGraph< GaussianFactor >::resize ( size_t size )

inlinevirtualinherited

Directly resize the number of factors in the graph. If the new size is less than the original, factors at the end will be removed. If the new size is larger than the original, null factors will be appended.

◆ size()

size_t gtsam::FactorGraph< GaussianFactor >::size ( ) const

inlineinherited

return the number of factors (including any null factors set by remove() ).

◆ sparseJacobian() [1/2]

std::vector<std::tuple<int, int, double> > gtsam::GaussianFactorGraph::sparseJacobian	(	const Ordering &	ordering,
		size_t &	nrows,
		size_t &	ncols
	)		const

Returns a sparse augmented Jacbian matrix as a vector of i, j, and s, where i(k) and j(k) are the base 0 row and column indices, and s(k) is the entry as a double. The standard deviations are baked into A and b

Returns: the sparse matrix as a std::vector of std::tuples

Parameters

	ordering	the column ordering
[out]	nrows	The number of rows in the augmented Jacobian
[out]	ncols	The number of columns in the augmented Jacobian

◆ sparseJacobian() [2/2]

std::vector<std::tuple<int, int, double> > gtsam::GaussianFactorGraph::sparseJacobian ( ) const

Returns a sparse augmented Jacobian matrix with default ordering

◆ sparseJacobian_()

Matrix gtsam::GaussianFactorGraph::sparseJacobian_ ( ) const

Matrix version of sparseJacobian: generates a 3*m matrix with [i,j,s] entries such that S(i(k),j(k)) = s(k), which can be given to MATLAB's sparse. Note: i, j are 1-indexed. The standard deviations are baked into A and b

◆ transposeMultiply()

VectorValues gtsam::GaussianFactorGraph::transposeMultiply ( const Errors & e ) const

x = A'*e

◆ transposeMultiplyAdd()

void gtsam::GaussianFactorGraph::transposeMultiplyAdd	(	double	alpha,
		const Errors &	e,
		VectorValues &	x
	)		const

x += alpha*A'*e

Member Data Documentation

◆ factors_

FastVector<sharedFactor> gtsam::FactorGraph< GaussianFactor >::factors_

protectedinherited

concept check, makes sure FACTOR defines print and equals Collection of factors

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

/home/docs/checkouts/readthedocs.org/user_builds/gtsam-jlblanco-docs/checkouts/latest/gtsam/linear/GaussianFactorGraph.h

Public Types

Public Member Functions

Protected Member Functions

Protected Attributes

Friends

Detailed Description

Member Typedef Documentation

◆ EliminationResult

◆ Keys

◆ OptionalVariableIndex

Constructor & Destructor Documentation

◆ GaussianFactorGraph() [1/5]

◆ GaussianFactorGraph() [2/5]

◆ GaussianFactorGraph() [3/5]

◆ GaussianFactorGraph() [4/5]

◆ GaussianFactorGraph() [5/5]

Member Function Documentation

◆ add() [1/8]

◆ add() [2/8]

◆ add() [3/8]

◆ add() [4/8]

◆ add() [5/8]

◆ add() [6/8]

◆ add() [7/8]

◆ add() [8/8]

◆ add_factors()

◆ at() [1/2]

◆ at() [2/2]

◆ augmentedHessian() [1/2]

◆ augmentedHessian() [2/2]

◆ augmentedJacobian() [1/2]

◆ augmentedJacobian() [2/2]

◆ back()

◆ begin() [1/2]

◆ begin() [2/2]

◆ clone()

◆ cloneToPtr()

◆ eliminateMultifrontal() [1/2]

◆ eliminateMultifrontal() [2/2]

◆ eliminatePartialMultifrontal() [1/2]

◆ eliminatePartialMultifrontal() [2/2]

◆ eliminatePartialSequential() [1/2]

◆ eliminatePartialSequential() [2/2]

◆ eliminateSequential() [1/2]

◆ eliminateSequential() [2/2]

◆ empty()

◆ end() [1/2]

◆ end() [2/2]

◆ erase() [1/2]

◆ erase() [2/2]

◆ error() [1/2]

◆ error() [2/2]

◆ exists()

◆ front()

◆ gaussianErrors()

◆ gradient()

◆ gradientAtZero()

◆ hessian() [1/2]

◆ hessian() [2/2]

◆ hessianBlockDiagonal()

◆ hessianDiagonal()

◆ jacobian() [1/2]

◆ jacobian() [2/2]

◆ keyVector()

◆ marginal()

◆ marginalMultifrontalBayesNet() [1/4]

◆ marginalMultifrontalBayesNet() [2/4]

◆ marginalMultifrontalBayesNet() [3/4]

◆ marginalMultifrontalBayesNet() [4/4]

◆ marginalMultifrontalBayesTree() [1/4]

◆ marginalMultifrontalBayesTree() [2/4]

◆ marginalMultifrontalBayesTree() [3/4]

◆ marginalMultifrontalBayesTree() [4/4]

◆ multiplyHessianAdd()

◆ multiplyInPlace() [1/2]

◆ multiplyInPlace() [2/2]

◆ negate()

◆ nrFactors()

◆ operator*()

◆ operator[]() [1/2]