gtsam  4.0.0 gtsam
gtsam::HessianFactor Class Reference

## Detailed Description

A Gaussian factor using the canonical parameters (information form)

HessianFactor implements a general quadratic factor of the form

$E(x) = 0.5 x^T G x - x^T g + 0.5 f$

that stores the matrix $$G$$, the vector $$g$$, and the constant term $$f$$.

When $$G$$ is positive semidefinite, this factor represents a Gaussian, in which case $$G$$ is the information matrix $$\Lambda$$, $$g$$ is the information vector $$\eta$$, and $$f$$ is the residual sum-square-error at the mean, when $$x = \mu$$.

Indeed, the negative log-likelihood of a Gaussian is (up to a constant) $$E(x) = 0.5(x-\mu)^T P^{-1} (x-\mu)$$ with $$\mu$$ the mean and $$P$$ the covariance matrix. Expanding the product we get

$E(x) = 0.5 x^T P^{-1} x - x^T P^{-1} \mu + 0.5 \mu^T P^{-1} \mu$

We define the Information matrix (or Hessian) $$\Lambda = P^{-1}$$ and the information vector $$\eta = P^{-1} \mu = \Lambda \mu$$ to arrive at the canonical form of the Gaussian:

$E(x) = 0.5 x^T \Lambda x - x^T \eta + 0.5 \mu^T \Lambda \mu$

This factor is one of the factors that can be in a GaussianFactorGraph. It may be returned from NonlinearFactor::linearize(), but is also used internally to store the Hessian during Cholesky elimination.

This can represent a quadratic factor with characteristics that cannot be represented using a JacobianFactor (which has the form $$E(x) = \Vert Ax - b \Vert^2$$ and stores the Jacobian $$A$$ and error vector $$b$$, i.e. is a sum-of-squares factor). For example, a HessianFactor need not be positive semidefinite, it can be indefinite or even negative semidefinite.

If a HessianFactor is indefinite or negative semi-definite, then in order for solving the linear system to be possible, the Hessian of the full system must be positive definite (i.e. when all small Hessians are combined, the result must be positive definite). If this is not the case, an error will occur during elimination.

This class stores G, g, and f as an augmented matrix HessianFactor::matrix_. The upper-left n x n blocks of HessianFactor::matrix_ store the upper-right triangle of G, the upper-right-most column of length n of HessianFactor::matrix_ stores g, and the lower-right entry of HessianFactor::matrix_ stores f, i.e.

HessianFactor::matrix_ = [ G11 G12 G13 ... g1
0 G22 G23 ... g2
0 0 G33 ... g3
: : : :
0 0 0 ... f ]

Blocks can be accessed as follows:

G11 = info(begin(), begin());
G12 = info(begin(), begin()+1);
G23 = info(begin()+1, begin()+2);
g2 = linearTerm(begin()+1);
.......
Inheritance diagram for gtsam::HessianFactor:

## Public Member Functions

HessianFactor ()
default constructor for I/O

HessianFactor (Key j, const Matrix &G, const Vector &g, double f)
Construct a unary factor. More...

HessianFactor (Key j, const Vector &mu, const Matrix &Sigma)
Construct a unary factor, given a mean and covariance matrix. More...

HessianFactor (Key j1, Key j2, const Matrix &G11, const Matrix &G12, const Vector &g1, const Matrix &G22, const Vector &g2, double f)
Construct a binary factor. More...

HessianFactor (Key j1, Key j2, Key j3, const Matrix &G11, const Matrix &G12, const Matrix &G13, const Vector &g1, const Matrix &G22, const Matrix &G23, const Vector &g2, const Matrix &G33, const Vector &g3, double f)
Construct a ternary factor. More...

HessianFactor (const KeyVector &js, const std::vector< Matrix > &Gs, const std::vector< Vector > &gs, double f)
Construct an n-way factor. More...

template<typename KEYS >
HessianFactor (const KEYS &keys, const SymmetricBlockMatrix &augmentedInformation)
Constructor with an arbitrary number of keys and with the augmented information matrix specified as a block matrix. More...

HessianFactor (const JacobianFactor &cg)
Construct from a JacobianFactor (or from a GaussianConditional since it derives from it)

HessianFactor (const GaussianFactor &factor)
Attempt to construct from any GaussianFactor - currently supports JacobianFactor, HessianFactor, GaussianConditional, or any derived classes. More...

HessianFactor (const GaussianFactorGraph &factors, boost::optional< const Scatter & > scatter=boost::none)
Combine a set of factors into a single dense HessianFactor.

virtual ~HessianFactor ()
Destructor.

virtual GaussianFactor::shared_ptr clone () const
Clone this HessianFactor.

virtual void print (const std::string &s="", const KeyFormatter &formatter=DefaultKeyFormatter) const
Print the factor for debugging and testing (implementing Testable)

virtual bool equals (const GaussianFactor &lf, double tol=1e-9) const
Compare to another factor for testing (implementing Testable)

virtual double error (const VectorValues &c) const
Evaluate the factor error f(x), see above. More...

virtual DenseIndex getDim (const_iterator variable) const
0.5*[x -1]'H[x -1] (also see constructor documentation) More...

size_t rows () const
Return the number of columns and rows of the Hessian matrix, including the information vector. More...

virtual GaussianFactor::shared_ptr negate () const
Construct the corresponding anti-factor to negate information stored stored in this factor. More...

virtual bool empty () const
Check if the factor is empty. More...

double constantTerm () const
Return the constant term $$f$$ as described above. More...

double & constantTerm ()
Return the constant term $$f$$ as described above. More...

SymmetricBlockMatrix::constBlock linearTerm (const_iterator j) const
Return the part of linear term $$g$$ as described above corresponding to the requested variable. More...

SymmetricBlockMatrix::constBlock linearTerm () const
Return the complete linear term $$g$$ as described above. More...

SymmetricBlockMatrix::Block linearTerm ()
Return the complete linear term $$g$$ as described above. More...

const SymmetricBlockMatrixinfo () const
Return underlying information matrix.

SymmetricBlockMatrixinfo ()
Return non-const information matrix. More...

virtual Matrix augmentedInformation () const
Return the augmented information matrix represented by this GaussianFactor. More...

Eigen::SelfAdjointView< SymmetricBlockMatrix::constBlock, Eigen::Upper > informationView () const
Return self-adjoint view onto the information matrix (NOT augmented).

virtual Matrix information () const
Return the non-augmented information matrix represented by this GaussianFactor.

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

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

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

virtual std::pair< Matrix, Vector > jacobian () const
Return (dense) matrix associated with factor.

virtual Matrix augmentedJacobian () const
Return (dense) matrix associated with factor The returned system is an augmented matrix: [A b]. More...

void updateHessian (const KeyVector &keys, SymmetricBlockMatrix *info) const
Update an information matrix by adding the information corresponding to this factor (used internally during elimination). More...

void updateHessian (HessianFactor *other) const
Update another Hessian factor. More...

void multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const
y += alpha * A'*A*x

VectorValues gradientAtZero () const
eta for Hessian

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

Vector gradient (Key key, const VectorValues &x) const
Compute the gradient at a key: \grad f(x_i) = \sum_j G_ij*x_j - g_i.

boost::shared_ptr< GaussianConditionaleliminateCholesky (const Ordering &keys)
In-place elimination that returns a conditional on (ordered) keys specified, and leaves this factor to be on the remaining keys (separator) only. More...

VectorValues solve ()
Solve the system A'*A delta = A'*b in-place, return delta as VectorValues.

Public Member Functions inherited from gtsam::GaussianFactor
GaussianFactor ()
Default constructor creates empty factor.

template<typename CONTAINER >
GaussianFactor (const CONTAINER &keys)
Construct from container of keys. More...

virtual ~GaussianFactor ()
Destructor.

Public Member Functions inherited from gtsam::Factor
Key front () const
First key.

Key back () const
Last key.

const_iterator find (Key key) const
find

const KeyVectorkeys () 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

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

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

KeyVectorkeys ()

iterator begin ()
Iterator at beginning of involved variable keys.

iterator end ()
Iterator at end of involved variable keys.

## Public Types

typedef GaussianFactor Base
Typedef to base class.

typedef HessianFactor This
Typedef to this class.

typedef boost::shared_ptr< Thisshared_ptr
A shared_ptr to this class.

typedef SymmetricBlockMatrix::Block Block
A block from the Hessian matrix.

typedef SymmetricBlockMatrix::constBlock constBlock
A block from the Hessian matrix (const version)

Public Types inherited from gtsam::GaussianFactor
typedef GaussianFactor This
This class.

typedef boost::shared_ptr< Thisshared_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.

## Protected Attributes

SymmetricBlockMatrix info_
The full augmented information matrix, s.t. the quadratic error is 0.5*[x -1]'H[x -1].

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

## Friends

class NonlinearFactorGraph

class NonlinearClusterTree

class boost::serialization::access
Serialization function.

## Additional Inherited Members

Static Public Member Functions inherited from gtsam::GaussianFactor
template<typename CONTAINER >
static DenseIndex Slot (const CONTAINER &keys, Key key)

Protected Member Functions inherited from gtsam::Factor
Factor ()
Default constructor for I/O.

template<typename CONTAINER >
Factor (const CONTAINER &keys)
Construct factor from container of keys. More...

template<typename ITERATOR >
Factor (ITERATOR first, ITERATOR last)
Construct factor from iterator keys. More...

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

Static Protected Member Functions inherited from gtsam::Factor
template<typename CONTAINER >
static Factor FromKeys (const CONTAINER &keys)
Construct factor from container of keys. More...

template<typename ITERATOR >
static Factor FromIterators (ITERATOR first, ITERATOR last)
Construct factor from iterator keys. More...

## ◆ HessianFactor() [1/7]

 gtsam::HessianFactor::HessianFactor ( Key j, const Matrix & G, const Vector & g, double f )

Construct a unary factor.

G is the quadratic term (Hessian matrix), g the linear term (a vector), and f the constant term. The quadratic error is: 0.5*(f - 2*x'*g + x'*G*x)

## ◆ HessianFactor() [2/7]

 gtsam::HessianFactor::HessianFactor ( Key j, const Vector & mu, const Matrix & Sigma )

Construct a unary factor, given a mean and covariance matrix.

error is 0.5*(x-mu)'inv(Sigma)(x-mu)

## ◆ HessianFactor() [3/7]

 gtsam::HessianFactor::HessianFactor ( Key j1, Key j2, const Matrix & G11, const Matrix & G12, const Vector & g1, const Matrix & G22, const Vector & g2, double f )

Construct a binary factor.

Gxx are the upper-triangle blocks of the quadratic term (the Hessian matrix), gx the pieces of the linear vector term, and f the constant term. JacobianFactor error is

$0.5* (Ax-b)' M (Ax-b) = 0.5*x'A'MAx - x'A'Mb + 0.5*b'Mb$

HessianFactor error is

$0.5*(x'Gx - 2x'g + f) = 0.5*x'Gx - x'*g + 0.5*f$

So, with $$A = [A1 A2]$$ and $$G=A*'M*A = [A1';A2']*M*[A1 A2]$$ we have

n1*n1 G11 = A1'*M*A1
n1*n2 G12 = A1'*M*A2
n2*n2 G22 = A2'*M*A2
n1*1 g1 = A1'*M*b
n2*1 g2 = A2'*M*b
1*1 f = b'*M*b

## ◆ HessianFactor() [4/7]

 gtsam::HessianFactor::HessianFactor ( Key j1, Key j2, Key j3, const Matrix & G11, const Matrix & G12, const Matrix & G13, const Vector & g1, const Matrix & G22, const Matrix & G23, const Vector & g2, const Matrix & G33, const Vector & g3, double f )

Construct a ternary factor.

Gxx are the upper-triangle blocks of the quadratic term (the Hessian matrix), gx the pieces of the linear vector term, and f the constant term.

## ◆ HessianFactor() [5/7]

 gtsam::HessianFactor::HessianFactor ( const KeyVector & js, const std::vector< Matrix > & Gs, const std::vector< Vector > & gs, double f )

Construct an n-way factor.

Gs contains the upper-triangle blocks of the quadratic term (the Hessian matrix) provided in row-order, gs the pieces of the linear vector term, and f the constant term.

## ◆ HessianFactor() [6/7]

template<typename KEYS >
 gtsam::HessianFactor::HessianFactor ( const KEYS & keys, const SymmetricBlockMatrix & augmentedInformation )

Constructor with an arbitrary number of keys and with the augmented information matrix specified as a block matrix.

## ◆ HessianFactor() [7/7]

 gtsam::HessianFactor::HessianFactor ( const GaussianFactor & factor )
explicit

Attempt to construct from any GaussianFactor - currently supports JacobianFactor, HessianFactor, GaussianConditional, or any derived classes.

## ◆ augmentedInformation()

 Matrix gtsam::HessianFactor::augmentedInformation ( ) const
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.

For HessianFactor, this is the same as info() except that this function returns a complete symmetric matrix whereas info() returns a matrix where only the upper triangle is valid, but should be interpreted as symmetric. This is because info() returns only a reference to the internal representation of the augmented information matrix, which stores only the upper triangle.

Implements gtsam::GaussianFactor.

## ◆ augmentedJacobian()

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

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

Parameters
 set weight to use whitening to bake in weights

Implements gtsam::GaussianFactor.

## ◆ constantTerm() [1/2]

 double gtsam::HessianFactor::constantTerm ( ) const
inline

Return the constant term $$f$$ as described above.

Returns
The constant term $$f$$

## ◆ constantTerm() [2/2]

 double& gtsam::HessianFactor::constantTerm ( )
inline

Return the constant term $$f$$ as described above.

Returns
The constant term $$f$$

## ◆ eliminateCholesky()

 boost::shared_ptr< GaussianConditional > gtsam::HessianFactor::eliminateCholesky ( const Ordering & keys )

In-place elimination that returns a conditional on (ordered) keys specified, and leaves this factor to be on the remaining keys (separator) only.

Does dense partial Cholesky.

## ◆ empty()

 virtual bool gtsam::HessianFactor::empty ( ) const
inlinevirtual

Check if the factor is empty.

TODO: How should this be defined?

Implements gtsam::GaussianFactor.

## ◆ error()

 double gtsam::HessianFactor::error ( const VectorValues & c ) const
virtual

Evaluate the factor error f(x), see above.

Implements gtsam::GaussianFactor.

## ◆ getDim()

 virtual DenseIndex gtsam::HessianFactor::getDim ( const_iterator variable ) const
inlinevirtual

0.5*[x -1]'H[x -1] (also see constructor documentation)

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?

Parameters
 variable An iterator pointing to the slot in this factor. You can use, for example, begin() + 2 to get the 3rd variable in this factor.

Implements gtsam::GaussianFactor.

## ◆ info()

 SymmetricBlockMatrix& gtsam::HessianFactor::info ( )
inline

Return non-const information matrix.

TODO(gareth): Review the sanity of having non-const access to this.

## ◆ linearTerm() [1/3]

 SymmetricBlockMatrix::constBlock gtsam::HessianFactor::linearTerm ( const_iterator j ) const
inline

Return the part of linear term $$g$$ as described above corresponding to the requested variable.

Parameters
 j Which block row to get, as an iterator pointing to the slot in this factor. You can use, for example, begin() + 2 to get the 3rd variable in this factor.
Returns
The linear term $$g$$

## ◆ linearTerm() [2/3]

 SymmetricBlockMatrix::constBlock gtsam::HessianFactor::linearTerm ( ) const
inline

Return the complete linear term $$g$$ as described above.

Returns
The linear term $$g$$

## ◆ linearTerm() [3/3]

 SymmetricBlockMatrix::Block gtsam::HessianFactor::linearTerm ( )
inline

Return the complete linear term $$g$$ as described above.

Returns
The linear term $$g$$

## ◆ negate()

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

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

Returns
a HessianFactor with negated Hessian matrices

Implements gtsam::GaussianFactor.

## ◆ rows()

 size_t gtsam::HessianFactor::rows ( ) const
inline

Return the number of columns and rows of the Hessian matrix, including the information vector.

## ◆ updateHessian() [1/2]

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

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

Parameters
 keys THe ordered vector of keys for the information matrix to be updated info The information matrix to be updated

Implements gtsam::GaussianFactor.

## ◆ updateHessian() [2/2]

 void gtsam::HessianFactor::updateHessian ( HessianFactor * other ) const
inline

Update another Hessian factor.

Parameters
 other the HessianFactor to be updated

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