gtsam  4.0.0 gtsam
gtsam::RegularImplicitSchurFactor< CAMERA > Class Template Reference

## Detailed Description

### template<class CAMERA> class gtsam::RegularImplicitSchurFactor< CAMERA >

Inheritance diagram for gtsam::RegularImplicitSchurFactor< CAMERA >:

## Public Member Functions

RegularImplicitSchurFactor ()
Constructor.

RegularImplicitSchurFactor (const KeyVector &keys, const std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > > &FBlocks, const Matrix &E, const Matrix &P, const Vector &b)
Construct from blocks of F, E, inv(E'*E), and RHS vector b.

virtual ~RegularImplicitSchurFactor ()
Destructor.

std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > > & FBlocks () const

const Matrix & E () const

const Vector & b () const

const Matrix & getPointCovariance () const

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

bool equals (const GaussianFactor &lf, double tol) const
equals

virtual DenseIndex getDim (const_iterator variable) const
Degrees of freedom of camera.

virtual 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...

virtual Matrix augmentedJacobian () const
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. More...

virtual std::pair< Matrix, Vector > jacobian () const
Return the dense Jacobian $$A$$ and right-hand-side $$b$$, with the noise models baked into A and b. More...

virtual Matrix augmentedInformation () const
Compute full augmented information matrix

virtual Matrix information () const
Compute full information matrix

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

virtual void hessianDiagonal (double *d) const
add the contribution of this factor to the diagonal of the hessian d(output) = d(input) + deltaHessianFactor

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

virtual GaussianFactor::shared_ptr clone () const
Clone a factor (make a deep copy)

virtual bool empty () const
Test whether the factor is empty.

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

void projectError2 (const Error2s &e1, Error2s &e2) const
Calculate corrected error Q*(e-ZDim*b) = (I - E*P*E')*(e-ZDim*b)

virtual double error (const VectorValues &x) const
Print for testable.

double errorJF (const VectorValues &x) const

void projectError (const Error2s &e1, Error2s &e2) const
Calculate corrected error Q*e = (I - E*P*E')*e.

void multiplyHessianAdd (double alpha, const double *x, double *y) const
double* Hessian-vector multiply, i.e. More...

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

void multiplyHessianAdd (double alpha, const VectorValues &x, VectorValues &y) const
Hessian-vector multiply, i.e. More...

void multiplyHessianDummy (double alpha, const VectorValues &x, VectorValues &y) const
Dummy version to measure overhead of key access.

Calculate gradient, which is -F'Q*b, see paper.

virtual void gradientAtZero (double *d) const
Calculate gradient, which is -F'Q*b, see paper - RAW MEMORY ACCESS.

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

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.

## Static Public Member Functions

static void multiplyHessianAdd (const Matrix &F, const Matrix &E, const Matrix &PointCovariance, double alpha, const Vector &x, Vector &y)

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

Error2s e1

Error2s e2

## Public Types

typedef RegularImplicitSchurFactor This
Typedef to this class.

typedef boost::shared_ptr< Thisshared_ptr
shared_ptr to this class

typedef std::vector< Vector2, Eigen::aligned_allocator< Vector2 > > Error2s

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 Types

typedef CameraSet< CAMERA > Set

typedef CAMERA::Measurement Z

typedef Eigen::Matrix< double, ZDim, DMatrixZD
type of an F block

typedef Eigen::Matrix< double, D, DMatrixDD
camera hessian

## Protected Attributes

const std::vector< MatrixZD, Eigen::aligned_allocator< MatrixZD > > FBlocks_
All ZDim*D F blocks (one for each camera)

const Matrix PointCovariance_
the 3*3 matrix P = inv(E'E) (2*2 if degenerate)

const Matrix E_
The 2m*3 E Jacobian with respect to the point.

const Vector b_
2m-dimensional RHS vector

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

## Static Protected Attributes

static const int D = traits<CAMERA>::dimension
Camera dimension.

static const int ZDim = traits<Z>::dimension
Measurement dimension.

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...

## ◆ augmentedJacobian()

template<class CAMERA >
 virtual Matrix gtsam::RegularImplicitSchurFactor< CAMERA >::augmentedJacobian ( ) const
inlinevirtual

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.

Implements gtsam::GaussianFactor.

## ◆ jacobian()

template<class CAMERA >
 virtual std::pair gtsam::RegularImplicitSchurFactor< CAMERA >::jacobian ( ) const
inlinevirtual

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.

Implements gtsam::GaussianFactor.

template<class CAMERA >
 void gtsam::RegularImplicitSchurFactor< CAMERA >::multiplyHessianAdd ( double alpha, const double * x, double * y ) const
inline

double* Hessian-vector multiply, i.e.

y += F'alpha(I - E*P*E')*F*x RAW memory access! Assumes keys start at 0 and go to M-1, and x and and y are laid out that way

template<class CAMERA >
 void gtsam::RegularImplicitSchurFactor< CAMERA >::multiplyHessianAdd ( double alpha, const VectorValues & x, VectorValues & y ) const
inlinevirtual

Hessian-vector multiply, i.e.

y += F'alpha(I - E*P*E')*F*x

Implements gtsam::GaussianFactor.

## ◆ negate()

template<class CAMERA >
 virtual GaussianFactor::shared_ptr gtsam::RegularImplicitSchurFactor< CAMERA >::negate ( ) const
inlinevirtual

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

Returns
a HessianFactor with negated Hessian matrices

Implements gtsam::GaussianFactor.

## ◆ updateHessian()

template<class CAMERA >
 virtual void gtsam::RegularImplicitSchurFactor< CAMERA >::updateHessian ( const KeyVector & keys, SymmetricBlockMatrix * info ) const
inlinevirtual

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.

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