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)

Inheritance diagram for gtsam::GaussianFactor:

Advanced Interface
virtual bool	isJacobian () const
	Fast check for JacobianFactor-based types.
template<typename CONTAINER>
static DenseIndex	Slot (const CONTAINER &keys, Key key)

Public Member Functions
Standard Constructors
	GaussianFactor ()
	Default constructor creates empty factor.
template<typename CONTAINER>
	GaussianFactor (const CONTAINER &keys)
	Construct from container of 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
virtual double	deltaError (const VectorValues &c, double oldError=nullptr, double newError=nullptr) const
	Compute the change in error from zero to c.
double	error (const HybridValues &c) const override
	All factor types need to implement an error function.
virtual DenseIndex	getDim (const_iterator variable) const =0
	Return the dimension of the variable pointed to by the given key iterator.
virtual Matrix	augmentedJacobian () const =0
	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.
virtual std::pair< Matrix, Vector >	jacobian () const =0
	Return the dense Jacobian \( A \) and right-hand-side \( b \), with the noise models baked into A and b.
virtual Matrix	augmentedInformation () const =0
	Return the augmented information matrix represented by this GaussianFactor.
virtual Matrix	information () const =0
	Return the non-augmented information matrix represented by this GaussianFactor.
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
	Clone a factor (make a deep copy).
virtual GaussianFactor::shared_ptr	negate () const =0
	Construct the corresponding anti-factor to negate information stored stored in this factor.
virtual void	updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info) const =0
	Update an information matrix by adding the information corresponding to this factor (used internally during elimination).
virtual void	updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info, DenseIndex beginCol, DenseIndex endCol) const =0
	Update an information matrix by adding the information corresponding to this factor (used internally during elimination), restricted to a range of block columns, useful for parallelization.
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.
Public Member Functions inherited from gtsam::Factor
virtual	~Factor ()=default
	Default destructor.
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
	Iterator at beginning of involved variable keys.
const_iterator	end () const
	Iterator at end of involved variable keys.
size_t	size () const
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
KeyVector &	keys ()
iterator	begin ()
	Iterator at beginning of involved variable keys.
iterator	end ()
	Iterator at end of involved variable keys.

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

Additional Inherited Members
	Factor ()
	Default constructor for I/O.
template<typename CONTAINER>
	Factor (const CONTAINER &keys)
	Construct factor from container of keys.
template<typename ITERATOR>
	Factor (ITERATOR first, ITERATOR last)
	Construct factor from iterator keys.
template<typename CONTAINER>
static Factor	FromKeys (const CONTAINER &keys)
	Construct factor from container of keys.
template<typename ITERATOR>
static Factor	FromIterators (ITERATOR first, ITERATOR last)
	Construct factor from iterator keys.
Protected Attributes inherited from gtsam::Factor
KeyVector	keys_
	The keys involved in this factor.

Constructor & Destructor Documentation

◆ GaussianFactor()

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::BatchJacobianFactorBase, 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::BatchJacobianFactorBase, 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::BatchJacobianFactor< ErrorDim, BlockDims >, gtsam::BatchJacobianFactor< ErrorDim, BlockDims... >, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ deltaError()

double GaussianFactor::deltaError	(	const VectorValues &	c,
		double *	oldError = nullptr,
		double *	newError = nullptr ) const

virtual

Compute the change in error from zero to c.

Optionally return the old and new errors for reuse by callers.

Reimplemented in gtsam::BatchJacobianFactor< ErrorDim, BlockDims >, gtsam::BatchJacobianFactor< ErrorDim, BlockDims... >, gtsam::BatchJacobianFactorBase, gtsam::FixedJacobianFactor< M, Ns >, gtsam::HessianFactor, and gtsam::JacobianFactor.

◆ 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::BatchJacobianFactorBase, gtsam::GaussianConditional, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ error() [1/2]

double GaussianFactor::error ( const HybridValues & hybridValues ) const

overridevirtual

All factor types need to implement an error function.

In factor graphs, this is the negative log-likelihood.

Reimplemented from gtsam::Factor.

Reimplemented in gtsam::HessianFactor, and gtsam::JacobianFactor.

◆ error() [2/2]

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

virtual

Reimplemented in gtsam::BatchJacobianFactorBase, gtsam::GaussianConditional, gtsam::HessianFactor, and gtsam::JacobianFactor.

◆ 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::BatchJacobianFactor< ErrorDim, BlockDims >, gtsam::BatchJacobianFactor< ErrorDim, BlockDims... >, gtsam::HessianFactor, gtsam::JacobianFactor, 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::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ gradientAtZero() [1/2]

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

pure virtual

A'*b for Jacobian, eta for Hessian.

Implemented in gtsam::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, gtsam::RegularImplicitSchurFactor< CAMERA >, 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::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, gtsam::RegularHessianFactor< D >, gtsam::RegularHessianFactor< Base::Dim >, gtsam::RegularImplicitSchurFactor< CAMERA >, 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::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ hessianDiagonal()

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

pure virtual

Raw memory access version of hessianDiagonal.

Implemented in gtsam::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, gtsam::RegularHessianFactor< D >, gtsam::RegularHessianFactor< Base::Dim >, gtsam::RegularImplicitSchurFactor< CAMERA >, 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::BatchJacobianFactor< ErrorDim, BlockDims >, gtsam::BatchJacobianFactor< ErrorDim, BlockDims... >, gtsam::BatchJacobianFactorBase, gtsam::FixedJacobianFactor< M, Ns >, 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::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ isJacobian()

virtual bool gtsam::GaussianFactor::isJacobian ( ) const

inlinevirtual

Fast check for JacobianFactor-based types.

Reimplemented in gtsam::JacobianFactor.

◆ 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::BatchJacobianFactorBase, 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::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, gtsam::RegularHessianFactor< D >, gtsam::RegularHessianFactor< Base::Dim >, gtsam::RegularImplicitSchurFactor< CAMERA >, 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::BatchJacobianFactorBase, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ 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::BatchJacobianFactorBase, gtsam::GaussianConditional, gtsam::GaussianDensity, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ updateHessian() [1/2]

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::BatchJacobianFactor< ErrorDim, BlockDims >, gtsam::BatchJacobianFactor< ErrorDim, BlockDims... >, gtsam::BatchJacobianFactorBase, gtsam::FixedJacobianFactor< M, Ns >, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

◆ updateHessian() [2/2]

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

pure virtual

Update an information matrix by adding the information corresponding to this factor (used internally during elimination), restricted to a range of block columns, useful for parallelization.

Parameters

keys	The ordered vector of keys for the information matrix to be updated
info	The information matrix to be updated
beginCol	First block column index (inclusive) in the range to update
endCol	Last block column index (exclusive) in the range to update

Implemented in gtsam::BatchJacobianFactor< ErrorDim, BlockDims >, gtsam::BatchJacobianFactor< ErrorDim, BlockDims... >, gtsam::BatchJacobianFactorBase, gtsam::FixedJacobianFactor< M, Ns >, gtsam::HessianFactor, gtsam::JacobianFactor, and gtsam::RegularImplicitSchurFactor< CAMERA >.

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

/tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/linear/GaussianFactor.h
/tmp/gtsam-4.3.0-doxygen.rsXPUS/source/gtsam/linear/GaussianFactor.cpp

Detailed Description

Advanced Interface

Public Member Functions

Public Types

Additional Inherited Members

Constructor & Destructor Documentation

◆ GaussianFactor()

Member Function Documentation

◆ augmentedInformation()

◆ augmentedJacobian()

◆ clone()

◆ deltaError()

◆ equals()

◆ error() [1/2]

◆ error() [2/2]

◆ getDim()

◆ gradient()

◆ gradientAtZero() [1/2]

◆ gradientAtZero() [2/2]

◆ hessianBlockDiagonal()

◆ hessianDiagonal()

◆ hessianDiagonalAdd()

◆ information()

◆ isJacobian()

◆ jacobian()

◆ multiplyHessianAdd()

◆ negate()

◆ print()

◆ updateHessian() [1/2]

◆ updateHessian() [2/2]