#include <JacobianFactor.h>

Inheritance diagram for gtsam::JacobianFactor:

[legend]

Collaboration diagram for gtsam::JacobianFactor:

Public Types
typedef JacobianFactor	This
	Typedef to this class.

typedef GaussianFactor	Base
	Typedef to base class.

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

typedef VerticalBlockMatrix::Block	ABlock

typedef VerticalBlockMatrix::constBlock	constABlock

typedef ABlock::ColXpr	BVector

typedef constABlock::ConstColXpr	constBVector

typedef KeyVector::iterator	iterator
	Iterator over keys.

typedef KeyVector::const_iterator	const_iterator
	Const iterator over keys.

Public Member Functions
	JacobianFactor (const GaussianFactor &gf)

	JacobianFactor (const JacobianFactor &jf)

	JacobianFactor (const HessianFactor &hf)

	JacobianFactor ()

	JacobianFactor (const Vector &b_in)

	JacobianFactor (Key i1, const Matrix &A1, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

	JacobianFactor (Key i1, const Matrix &A1, Key i2, const Matrix &A2, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

	JacobianFactor (Key i1, const Matrix &A1, Key i2, const Matrix &A2, Key i3, const Matrix &A3, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

template<typename TERMS >
	JacobianFactor (const TERMS &terms, const Vector &b, const SharedDiagonal &model=SharedDiagonal())

template<typename KEYS >
	JacobianFactor (const KEYS &keys, const VerticalBlockMatrix &augmentedMatrix, const SharedDiagonal &sigmas=SharedDiagonal())

	JacobianFactor (const GaussianFactorGraph &graph)

	JacobianFactor (const GaussianFactorGraph &graph, const VariableSlots &p_variableSlots)

	JacobianFactor (const GaussianFactorGraph &graph, const Ordering &ordering)

	JacobianFactor (const GaussianFactorGraph &graph, const Ordering &ordering, const VariableSlots &p_variableSlots)

	~JacobianFactor () override

GaussianFactor::shared_ptr	clone () const override

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

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

Vector	unweighted_error (const VectorValues &c) const

Vector	error_vector (const VectorValues &c) const

double	error (const VectorValues &c) const override

Matrix	augmentedInformation () const override

Matrix	information () const override

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

void	hessianDiagonal (double *d) const override
	Raw memory access version of hessianDiagonal.

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

std::pair< Matrix, Vector >	jacobian () const override
	Returns (dense) A,b pair associated with factor, bakes in the weights.

std::pair< Matrix, Vector >	jacobianUnweighted () const
	Returns (dense) A,b pair associated with factor, does not bake in weights.

Matrix	augmentedJacobian () const override

Matrix	augmentedJacobianUnweighted () const

const VerticalBlockMatrix &	matrixObject () const

VerticalBlockMatrix &	matrixObject ()

GaussianFactor::shared_ptr	negate () const override

bool	isConstrained () const

DenseIndex	getDim (const_iterator variable) const override

size_t	rows () const

size_t	cols () const

const SharedDiagonal &	get_model () const

SharedDiagonal &	get_model ()

const constBVector	getb () const

constABlock	getA (const_iterator variable) const

constABlock	getA () const

BVector	getb ()

ABlock	getA (iterator variable)

ABlock	getA ()

void	updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info) const override

Vector	operator* (const VectorValues &x) const

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

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

void	multiplyHessianAdd (double alpha, const double x, double y, const std::vector< size_t > &accumulatedDims) const

VectorValues	gradientAtZero () const override
	A'*b for Jacobian.

void	gradientAtZero (double *d) const override
	A'*b for Jacobian (raw memory version)

Vector	gradient (Key key, const VectorValues &x) const override
	Compute the gradient wrt a key at any values.

JacobianFactor	whiten () const

std::pair< std::shared_ptr< GaussianConditional >, shared_ptr >	eliminate (const Ordering &keys)

void	setModel (bool anyConstrained, const Vector &sigmas)

std::shared_ptr< GaussianConditional >	splitConditional (size_t nrFrontals)

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

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

Standard Interface
double	error (const HybridValues &c) const override

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

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 ()

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

Protected Member Functions
template<typename TERMS >
void	fillTerms (const TERMS &terms, const Vector &b, const SharedDiagonal &noiseModel)
	Internal function to fill blocks and set dimensions.

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
VerticalBlockMatrix	Ab_

noiseModel::Diagonal::shared_ptr	model_

KeyVector	keys_
	The keys involved in this factor.

Friends
template<typename T >
class	ExpressionFactor

GTSAM_EXPORT std::pair< std::shared_ptr< GaussianConditional >, shared_ptr >	EliminateQR (const GaussianFactorGraph &factors, const Ordering &keys)

Detailed Description

A Gaussian factor in the squared-error form.

JacobianFactor implements a Gaussian, which has quadratic negative log-likelihood

\[ E(x) = \frac{1}{2} (Ax-b)^T \Sigma^{-1} (Ax-b) \]

where \( \Sigma \) is a diagonal covariance matrix. The matrix \( A \), r.h.s. vector \( b \), and diagonal noise model \( \Sigma \) are stored in this class.

This factor represents the sum-of-squares error of a linear measurement function, and is created upon linearization of a NoiseModelFactor, which in turn is a sum-of-squares factor with a nonlinear measurement function.

Here is an example of how this factor represents a sum-of-squares error:

Letting \( h(x) \) be a linear measurement prediction function, \( z \) be the actual observed measurement, the residual is

\[ f(x) = h(x) - z . \]

If we expect noise with diagonal covariance matrix \( \Sigma \) on this measurement, then the negative log-likelihood of the Gaussian induced by this measurement model is

\[ E(x) = \frac{1}{2} (h(x) - z)^T \Sigma^{-1} (h(x) - z) . \]

Because \( h(x) \) is linear, we can write it as

\[ h(x) = Ax + e \]

and thus we have

\[ E(x) = \frac{1}{2} (Ax-b)^T \Sigma^{-1} (Ax-b) \]

where \( b = z - e \).

This factor can involve an arbitrary number of variables, and in the above example \( x \) would almost always be only be a subset of the variables in the entire factor graph. There are special constructors for 1-, 2-, and 3- way JacobianFactors, and additional constructors for creating n-way JacobianFactors. The Jacobian matrix \( A \) is passed to these constructors in blocks, for example, for a 2-way factor, the constructor would accept \( A1 \) and \( A2 \), as well as the variable indices \( j1 \) and \( j2 \) and the negative log-likelihood represented by this factor would be

\[ E(x) = \frac{1}{2} (A_1 x_{j1} + A_2 x_{j2} - b)^T \Sigma^{-1} (A_1 x_{j1} + A_2 x_{j2} - b) . \]

HessianFactor, which represent a Gaussian likelihood over a set of variables.

Discrete factors, such as

Constructor & Destructor Documentation

◆ JacobianFactor() [1/14]

gtsam::JacobianFactor::JacobianFactor ( const GaussianFactor & gf )

explicit

Convert from other GaussianFactor

◆ JacobianFactor() [2/14]

gtsam::JacobianFactor::JacobianFactor ( const JacobianFactor & jf )

inline

Copy constructor

◆ JacobianFactor() [3/14]

gtsam::JacobianFactor::JacobianFactor ( const HessianFactor & hf )

explicit

Conversion from HessianFactor (does Cholesky to obtain Jacobian matrix)

◆ JacobianFactor() [4/14]

gtsam::JacobianFactor::JacobianFactor ( )

default constructor for I/O

◆ JacobianFactor() [5/14]

gtsam::JacobianFactor::JacobianFactor ( const Vector & b_in )

explicit

Construct Null factor

◆ JacobianFactor() [6/14]

gtsam::JacobianFactor::JacobianFactor	(	Key	i1,
		const Matrix &	A1,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

Construct unary factor

◆ JacobianFactor() [7/14]

gtsam::JacobianFactor::JacobianFactor	(	Key	i1,
		const Matrix &	A1,
		Key	i2,
		const Matrix &	A2,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

Construct binary factor

◆ JacobianFactor() [8/14]

gtsam::JacobianFactor::JacobianFactor	(	Key	i1,
		const Matrix &	A1,
		Key	i2,
		const Matrix &	A2,
		Key	i3,
		const Matrix &	A3,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

Construct ternary factor

◆ JacobianFactor() [9/14]

template<typename TERMS >

JacobianFactor::JacobianFactor	(	const TERMS &	terms,
		const Vector &	b,
		const SharedDiagonal &	model = `SharedDiagonal()`
	)

Construct an n-ary factor

Template Parameters

TERMS A container whose value type is std::pair<Key, Matrix>, specifying the collection of keys and matrices making up the factor.

◆ JacobianFactor() [10/14]

template<typename KEYS >

JacobianFactor::JacobianFactor	(	const KEYS &	keys,
		const VerticalBlockMatrix &	augmentedMatrix,
		const SharedDiagonal &	sigmas = `SharedDiagonal()`
	)

Constructor with arbitrary number keys, and where the augmented matrix is given all together instead of in block terms. Note that only the active view of the provided augmented matrix is used, and that the matrix data is copied into a newly-allocated matrix in the constructed factor.

◆ JacobianFactor() [11/14]

gtsam::JacobianFactor::JacobianFactor ( const GaussianFactorGraph & graph )

explicit

Build a dense joint factor from all the factors in a factor graph. If a VariableSlots structure computed for graph is already available, providing it will reduce the amount of computation performed.

◆ JacobianFactor() [12/14]

gtsam::JacobianFactor::JacobianFactor	(	const GaussianFactorGraph &	graph,
		const VariableSlots &	p_variableSlots
	)

explicit

Build a dense joint factor from all the factors in a factor graph. If a VariableSlots structure computed for graph is already available, providing it will reduce the amount of computation performed.

◆ JacobianFactor() [13/14]

gtsam::JacobianFactor::JacobianFactor	(	const GaussianFactorGraph &	graph,
		const Ordering &	ordering
	)

explicit

Build a dense joint factor from all the factors in a factor graph. If a VariableSlots structure computed for graph is already available, providing it will reduce the amount of computation performed.

◆ JacobianFactor() [14/14]

gtsam::JacobianFactor::JacobianFactor	(	const GaussianFactorGraph &	graph,
		const Ordering &	ordering,
		const VariableSlots &	p_variableSlots
	)

explicit

Build a dense joint factor from all the factors in a factor graph. If a VariableSlots structure computed for graph is already available, providing it will reduce the amount of computation performed.

◆ ~JacobianFactor()

gtsam::JacobianFactor::~JacobianFactor ( )

inlineoverride

Virtual destructor

Member Function Documentation

◆ augmentedInformation()

Matrix gtsam::JacobianFactor::augmentedInformation ( ) const

overridevirtual

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.

Implements gtsam::GaussianFactor.

◆ augmentedJacobian()

Matrix gtsam::JacobianFactor::augmentedJacobian ( ) const

overridevirtual

Return (dense) matrix associated with factor. The returned system is an augmented matrix: [A b] weights are baked in

Implements gtsam::GaussianFactor.

◆ augmentedJacobianUnweighted()

Matrix gtsam::JacobianFactor::augmentedJacobianUnweighted ( ) const

Return (dense) matrix associated with factor. The returned system is an augmented matrix: [A b] weights are not baked in

◆ 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()

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

inlineoverridevirtual

Clone this JacobianFactor

Implements gtsam::GaussianFactor.

Reimplemented in gtsam::LinearInequality, gtsam::LinearEquality, and gtsam::LinearCost.

◆ cols()

size_t gtsam::JacobianFactor::cols ( ) const

inline

return the number of columns in the corresponding linear system

◆ eliminate()

std::pair<std::shared_ptr<GaussianConditional>, shared_ptr> gtsam::JacobianFactor::eliminate ( const Ordering & keys )

Eliminate the requested variables.

◆ 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

overridevirtualinherited

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

Reimplemented from gtsam::Factor.

◆ error_vector()

Vector gtsam::JacobianFactor::error_vector ( const VectorValues & c ) const

(A*x-b)

◆ 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.

◆ get_model() [1/2]

const SharedDiagonal& gtsam::JacobianFactor::get_model ( ) const

inline

get a copy of model

◆ get_model() [2/2]

SharedDiagonal& gtsam::JacobianFactor::get_model ( )

inline

get a copy of model (non-const version)

◆ getA() [1/4]

constABlock gtsam::JacobianFactor::getA ( const_iterator variable ) const

inline

Get a view of the A matrix for the variable pointed to by the given key iterator

◆ getA() [2/4]

constABlock gtsam::JacobianFactor::getA ( ) const

inline

Get a view of the A matrix, not weighted by noise

◆ getA() [3/4]

ABlock gtsam::JacobianFactor::getA ( iterator variable )

inline

Get a view of the A matrix for the variable pointed to by the given key iterator (non-const version)

◆ getA() [4/4]

ABlock gtsam::JacobianFactor::getA ( )

inline

Get a view of the A matrix

◆ getb() [1/2]

const constBVector gtsam::JacobianFactor::getb ( ) const

inline

Get a view of the r.h.s. vector b, not weighted by noise

◆ getb() [2/2]

BVector gtsam::JacobianFactor::getb ( )

inline

Get a view of the r.h.s. vector b (non-const version)

◆ getDim()

DenseIndex gtsam::JacobianFactor::getDim ( const_iterator variable ) const

inlineoverridevirtual

Return the dimension of the variable pointed to by the given key iterator todo: Remove this in favor of keeping track of dimensions with variables?

Implements gtsam::GaussianFactor.

◆ information()

Matrix gtsam::JacobianFactor::information ( ) const

overridevirtual

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

Implements gtsam::GaussianFactor.

◆ isConstrained()

bool gtsam::JacobianFactor::isConstrained ( ) const

inline

is noise model constrained ?

◆ keys()

KeyVector& gtsam::Factor::keys ( )

inlineinherited

Returns: keys involved in this factor

◆ matrixObject() [1/2]

const VerticalBlockMatrix& gtsam::JacobianFactor::matrixObject ( ) const

inline

Return the full augmented Jacobian matrix of this factor as a VerticalBlockMatrix object.

◆ matrixObject() [2/2]

VerticalBlockMatrix& gtsam::JacobianFactor::matrixObject ( )

inline

Mutable access to the full augmented Jacobian matrix of this factor as a VerticalBlockMatrix object.

◆ multiplyHessianAdd() [1/2]

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

overridevirtual

y += alpha * A'*A*x

Implements gtsam::GaussianFactor.

Reimplemented in gtsam::RegularJacobianFactor< D >.

◆ multiplyHessianAdd() [2/2]

void gtsam::JacobianFactor::multiplyHessianAdd	(	double	alpha,
		const double *	x,
		double *	y,
		const std::vector< size_t > &	accumulatedDims
	)		const

Raw memory access version of multiplyHessianAdd y += alpha * A'*A*x Requires the vector accumulatedDims to tell the dimension of each variable: e.g.: x0 has dim 3, x2 has dim 6, x3 has dim 2, then accumulatedDims is [0 3 9 11 13] NOTE: size of accumulatedDims is size of keys + 1!! TODO(frank): we should probably kill this if no longer needed

◆ negate()

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

overridevirtual

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

Returns: a HessianFactor with negated Hessian matrices

Implements gtsam::GaussianFactor.

◆ operator*()

Vector gtsam::JacobianFactor::operator* ( const VectorValues & x ) const

Return A*x

◆ rows()

size_t gtsam::JacobianFactor::rows ( ) const

inline

return the number of rows in the corresponding linear system

◆ setModel()

void gtsam::JacobianFactor::setModel	(	bool	anyConstrained,
		const Vector &	sigmas
	)

set noiseModel correctly

◆ size()

size_t gtsam::Factor::size ( ) const

inlineinherited

Returns: the number of variables involved in this factor

◆ splitConditional()

std::shared_ptr<GaussianConditional> gtsam::JacobianFactor::splitConditional ( size_t nrFrontals )

splits a pre-factorized factor into a conditional, and changes the current factor to be the remaining component. Performs same operation as eliminate(), but without running QR. NOTE: looks at dimension of noise model to determine how many rows to keep.

Parameters

nrFrontals number of keys to eliminate

◆ transposeMultiplyAdd()

void gtsam::JacobianFactor::transposeMultiplyAdd	(	double	alpha,
		const Vector &	e,
		VectorValues &	x
	)		const

x += alpha * A'*e. If x is initially missing any values, they are created and assumed to start as zero vectors.

◆ updateHessian()

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

overridevirtual

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

Implements gtsam::GaussianFactor.

◆ whiten()

JacobianFactor gtsam::JacobianFactor::whiten ( ) const

Return a whitened version of the factor, i.e. with unit diagonal noise model.

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

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

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Static Protected Member Functions

Protected Attributes

Friends

Detailed Description

Constructor & Destructor Documentation

◆ JacobianFactor() [1/14]

◆ JacobianFactor() [2/14]

◆ JacobianFactor() [3/14]

◆ JacobianFactor() [4/14]

◆ JacobianFactor() [5/14]

◆ JacobianFactor() [6/14]

◆ JacobianFactor() [7/14]

◆ JacobianFactor() [8/14]

◆ JacobianFactor() [9/14]

◆ JacobianFactor() [10/14]

◆ JacobianFactor() [11/14]

◆ JacobianFactor() [12/14]

◆ JacobianFactor() [13/14]

◆ JacobianFactor() [14/14]

◆ ~JacobianFactor()

Member Function Documentation

◆ augmentedInformation()

◆ augmentedJacobian()

◆ augmentedJacobianUnweighted()

◆ begin() [1/2]

◆ begin() [2/2]

◆ clone()

◆ cols()

◆ eliminate()

◆ end() [1/2]

◆ end() [2/2]

◆ error()

◆ error_vector()

◆ FromIterators()

◆ FromKeys()

◆ get_model() [1/2]

◆ get_model() [2/2]

◆ getA() [1/4]

◆ getA() [2/4]

◆ getA() [3/4]

◆ getA() [4/4]

◆ getb() [1/2]

◆ getb() [2/2]

◆ getDim()

◆ information()

◆ isConstrained()

◆ keys()

◆ matrixObject() [1/2]

◆ matrixObject() [2/2]

◆ multiplyHessianAdd() [1/2]

◆ multiplyHessianAdd() [2/2]

◆ negate()

◆ operator*()

◆ rows()

◆ setModel()

◆ size()

◆ splitConditional()

◆ transposeMultiplyAdd()

◆ updateHessian()

◆ whiten()