#include <GaussianFactor.h>

Inheritance diagram for gtsam::GaussianFactor:

[legend]

Collaboration diagram for gtsam::GaussianFactor:

Public Types
typedef GaussianFactor	This
	This class.

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

typedef Factor	Base
	Our base class.

typedef KeyVector::iterator	iterator
	Iterator over keys.

typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys.

Public Member Functions
Standard Constructors
	GaussianFactor ()

template<typename CONTAINER >
	GaussianFactor (const CONTAINER &keys)

Testable
void	print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const override=0
	print with optional string

virtual bool	equals (const GaussianFactor &lf, double tol=1e-9) const =0
	assert equality up to a tolerance

Standard Interface
virtual double	error (const VectorValues &c) const

double	error (const HybridValues &c) const override

virtual DenseIndex	getDim (const_iterator variable) const =0

virtual Matrix	augmentedJacobian () const =0

virtual std::pair< Matrix, Vector >	jacobian () const =0

virtual Matrix	augmentedInformation () const =0

virtual Matrix	information () const =0

VectorValues	hessianDiagonal () const
	Return the diagonal of the Hessian for this factor.

virtual void	hessianDiagonalAdd (VectorValues &d) const =0
	Add the current diagonal to a VectorValues instance.

virtual void	hessianDiagonal (double *d) const =0
	Raw memory access version of hessianDiagonal.

virtual std::map< Key, Matrix >	hessianBlockDiagonal () const =0
	Return the block diagonal of the Hessian for this factor.

virtual GaussianFactor::shared_ptr	clone () const =0

virtual GaussianFactor::shared_ptr	negate () const =0

virtual void	updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info) const =0

Operator interface
virtual void	multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const =0
	y += alpha * A'Ax

virtual VectorValues	gradientAtZero () const =0
	A'*b for Jacobian, eta for Hessian.

virtual void	gradientAtZero (double *d) const =0
	Raw memory access version of gradientAtZero.

virtual Vector	gradient (Key key, const VectorValues &x) const =0
	Gradient wrt a key at any values.

Standard Interface
bool	empty () const
	Whether the factor is empty (involves zero variables).

Key	front () const
	First key.

Key	back () const
	Last key.

const_iterator	find (Key key) const
	find

const KeyVector &	keys () const
	Access the factor's involved variable keys.

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

Advanced Interface
KeyVector &	keys ()

iterator	begin ()

iterator	end ()

Testable
virtual void	printKeys (const std::string &s="Factor", const KeyFormatter &formatter=DefaultKeyFormatter) const
	print only keys

bool	equals (const This &other, double tol=1e-9) const
	check equality

Static Public Member Functions
Advanced Interface
template<typename CONTAINER >
static DenseIndex	Slot (const CONTAINER &keys, Key key)

Static Protected Member Functions
Standard Constructors
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

template<typename ITERATOR >
static Factor	FromIterators (ITERATOR first, ITERATOR last)

Protected Attributes
KeyVector	keys_
	The keys involved in this factor.

Detailed Description

An abstract virtual base class for JacobianFactor and HessianFactor. A GaussianFactor has a quadratic error function. GaussianFactor is non-mutable (all methods const!). The factor value is exp(-0.5*||Ax-b||^2)

Constructor & Destructor Documentation

◆ GaussianFactor() [1/2]

gtsam::GaussianFactor::GaussianFactor ( )

inline

Default constructor creates empty factor

◆ GaussianFactor() [2/2]

template<typename CONTAINER >

gtsam::GaussianFactor::GaussianFactor ( const CONTAINER & keys )

inline

Construct from container of keys. This constructor is used internally from derived factor constructors, either from a container of keys or from a boost::assign::list_of.

Member Function Documentation

◆ augmentedInformation()

virtual Matrix gtsam::GaussianFactor::augmentedInformation ( ) const

pure virtual

Return the augmented information matrix represented by this GaussianFactor. The augmented information matrix contains the information matrix with an additional column holding the information vector, and an additional row holding the transpose of the information vector. The lower-right entry contains the constant error term (when \( \delta x = 0 \)). The augmented information matrix is described in more detail in HessianFactor, which in fact stores an augmented information matrix.

Implemented in gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ augmentedJacobian()

virtual Matrix gtsam::GaussianFactor::augmentedJacobian ( ) const

pure virtual

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.

Implemented in gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ begin() [1/2]

const_iterator gtsam::Factor::begin ( ) const

inlineinherited

Iterator at beginning of involved variable keys

◆ begin() [2/2]

iterator gtsam::Factor::begin ( )

inlineinherited

Iterator at beginning of involved variable keys

◆ clone()

virtual GaussianFactor::shared_ptr gtsam::GaussianFactor::clone ( ) const

pure virtual

Clone a factor (make a deep copy)

Implemented in gtsam::RegularImplicitSchurFactor< CAMERA >, gtsam::JacobianFactor, gtsam::HessianFactor, gtsam::LinearInequality, gtsam::LinearEquality, and gtsam::LinearCost.

◆ end() [1/2]

const_iterator gtsam::Factor::end ( ) const

inlineinherited

Iterator at end of involved variable keys

◆ end() [2/2]

iterator gtsam::Factor::end ( )

inlineinherited

Iterator at end of involved variable keys

◆ error()

double gtsam::GaussianFactor::error ( const HybridValues & c ) const

overridevirtual

All factor types need to implement an error function. In factor graphs, this is the negative log-likelihood.

Reimplemented from gtsam::Factor.

◆ FromIterators()

template<typename ITERATOR >

static Factor gtsam::Factor::FromIterators	(	ITERATOR	first,
		ITERATOR	last
	)

inlinestaticprotectedinherited

Construct factor from iterator keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ FromKeys()

template<typename CONTAINER >

static Factor gtsam::Factor::FromKeys ( const CONTAINER & keys )

inlinestaticprotectedinherited

Construct factor from container of keys. This is called internally from derived factor static factor methods, as a workaround for not being able to call the protected constructors above.

◆ getDim()

virtual DenseIndex gtsam::GaussianFactor::getDim ( const_iterator variable ) const

pure virtual

Return the dimension of the variable pointed to by the given key iterator

Implemented in gtsam::JacobianFactor, gtsam::HessianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ information()

virtual Matrix gtsam::GaussianFactor::information ( ) const

pure virtual

Return the non-augmented information matrix represented by this GaussianFactor.

Implemented in gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ jacobian()

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

pure virtual

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.

Implemented in gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ keys()

KeyVector& gtsam::Factor::keys ( )

inlineinherited

Returns: keys involved in this factor

◆ negate()

virtual GaussianFactor::shared_ptr gtsam::GaussianFactor::negate ( ) const

pure virtual

Construct the corresponding anti-factor to negate information stored stored in this factor.

Returns: a HessianFactor with negated Hessian matrices

Implemented in gtsam::JacobianFactor, gtsam::RegularImplicitSchurFactor< CAMERA >, and gtsam::HessianFactor.

◆ size()

size_t gtsam::Factor::size ( ) const

inlineinherited

Returns: the number of variables involved in this factor

◆ updateHessian()

virtual void gtsam::GaussianFactor::updateHessian	(	const KeyVector &	keys,
		SymmetricBlockMatrix *	info
	)		const

pure virtual

Update an information matrix by adding the information corresponding to this factor (used internally during elimination).

Parameters

scatter	A mapping from variable index to slot index in this HessianFactor
info	The information matrix to be updated

Implemented in gtsam::HessianFactor, gtsam::JacobianFactor, gtsam::RegularImplicitSchurFactor< CAMERA >, and gtsam::BinaryJacobianFactor< M, N1, N2 >.

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/GaussianFactor.h

Public Types

Public Member Functions

Static Public Member Functions

Static Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ GaussianFactor() [1/2]

◆ GaussianFactor() [2/2]

Member Function Documentation

◆ augmentedInformation()

◆ augmentedJacobian()

◆ begin() [1/2]

◆ begin() [2/2]

◆ clone()

◆ end() [1/2]

◆ end() [2/2]

◆ error()

◆ FromIterators()

◆ FromKeys()

◆ getDim()

◆ information()

◆ jacobian()

◆ keys()

◆ negate()

◆ size()

◆ updateHessian()