#include <GaussianFactor.h>

Inheritance diagram for gtsam::GaussianFactor:

[legend]

Public Types
typedef Factor	Base
	Our base class. More...

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

typedef GaussianFactor	This
	This class. More...

Public Types inherited from gtsam::Factor
typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys. More...

typedef KeyVector::iterator	iterator
	Iterator over keys. More...

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 More...

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

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. More...

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

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

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

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 More...

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

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

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

Public Member Functions inherited from gtsam::Factor
virtual	~Factor ()=default
	Default destructor. More...

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

Key	front () const
	First key. More...

Key	back () const
	Last key. More...

const_iterator	find (Key key) const
	find More...

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

const_iterator	begin () const

const_iterator	end () const

size_t	size () const

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

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

KeyVector &	keys ()

iterator	begin ()

iterator	end ()

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

Additional Inherited Members
Protected Member Functions inherited from gtsam::Factor
	Factor ()

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

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

Static Protected Member Functions inherited from gtsam::Factor
template<typename CONTAINER >
static Factor	FromKeys (const CONTAINER &keys)

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

Protected Attributes inherited from gtsam::Factor
KeyVector	keys_
	The keys involved in this factor. More...

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)

Definition at line 38 of file GaussianFactor.h.

Member Typedef Documentation

◆ Base

typedef Factor gtsam::GaussianFactor::Base

Our base class.

Definition at line 43 of file GaussianFactor.h.

◆ shared_ptr

typedef std::shared_ptr<This> gtsam::GaussianFactor::shared_ptr

shared_ptr to this class

Definition at line 42 of file GaussianFactor.h.

◆ This

typedef GaussianFactor gtsam::GaussianFactor::This

This class.

Definition at line 41 of file GaussianFactor.h.

Constructor & Destructor Documentation

◆ GaussianFactor() [1/2]

gtsam::GaussianFactor::GaussianFactor ( )

inline

Default constructor creates empty factor

Definition at line 49 of file GaussianFactor.h.

◆ 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.

Definition at line 54 of file GaussianFactor.h.

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 >.

◆ 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, gtsam::LinearCost, and gtsam::DummyFactor< CAMERA >.

◆ equals()

virtual bool gtsam::GaussianFactor::equals	(	const GaussianFactor &	lf,
		double	tol = `1e-9`
	)		const

pure virtual

assert equality up to a tolerance

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

◆ error() [1/2]

double GaussianFactor::error ( const VectorValues & c ) const

virtual

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

Definition at line 27 of file GaussianFactor.cpp.

◆ error() [2/2]

double 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.

Definition at line 31 of file GaussianFactor.cpp.

◆ 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 >.

◆ gradient()

virtual Vector gtsam::GaussianFactor::gradient	(	Key	key,
		const VectorValues &	x
	)		const

pure virtual

Gradient wrt a key at any values.

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

◆ gradientAtZero() [1/2]

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

pure virtual

A'*b for Jacobian, eta for Hessian.

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

◆ gradientAtZero() [2/2]

virtual void gtsam::GaussianFactor::gradientAtZero ( double * d ) const

pure virtual

Raw memory access version of gradientAtZero.

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

◆ hessianBlockDiagonal()

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

pure virtual

Return the block diagonal of the Hessian for this factor.

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

◆ hessianDiagonal() [1/2]

VectorValues GaussianFactor::hessianDiagonal ( ) const

Return the diagonal of the Hessian for this factor.

Definition at line 35 of file GaussianFactor.cpp.

◆ hessianDiagonal() [2/2]

virtual void gtsam::GaussianFactor::hessianDiagonal ( double * d ) const

pure virtual

Raw memory access version of hessianDiagonal.

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

◆ hessianDiagonalAdd()

virtual void gtsam::GaussianFactor::hessianDiagonalAdd ( VectorValues & d ) const

pure virtual

Add the current diagonal to a VectorValues instance.

Implemented in gtsam::HessianFactor, gtsam::JacobianFactor, 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 >.

◆ multiplyHessianAdd()

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

pure virtual

y += alpha * A'*A*x

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

◆ 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.

◆ print()

void gtsam::GaussianFactor::print	(	const std::string &	s = `""`,
		const KeyFormatter &	formatter = `DefaultKeyFormatter`
	)		const

overridepure virtual

print with optional string

Reimplemented from gtsam::Factor.

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

◆ Slot()

template<typename CONTAINER >

static DenseIndex gtsam::GaussianFactor::Slot	(	const CONTAINER &	keys,
		Key	key
	)

inlinestatic

Definition at line 175 of file GaussianFactor.h.

◆ 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 files:

Public Types

Public Member Functions

Static Public Member Functions

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ Base

◆ shared_ptr

◆ This

Constructor & Destructor Documentation

◆ GaussianFactor() [1/2]

◆ GaussianFactor() [2/2]

Member Function Documentation

◆ augmentedInformation()

◆ augmentedJacobian()

◆ clone()

◆ equals()

◆ error() [1/2]

◆ error() [2/2]

◆ getDim()

◆ gradient()

◆ gradientAtZero() [1/2]

◆ gradientAtZero() [2/2]

◆ hessianBlockDiagonal()

◆ hessianDiagonal() [1/2]

◆ hessianDiagonal() [2/2]

◆ hessianDiagonalAdd()

◆ information()

◆ jacobian()

◆ multiplyHessianAdd()

◆ negate()

◆ print()

◆ Slot()

◆ updateHessian()