gtsam 4.2.0
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gtsam::noiseModel::mEstimator::Huber Class Reference

Detailed Description

The "Huber" robust error model (Zhang97ivc).

This model has a scalar parameter "k".

  • Loss \rho(x) = 0.5 x² if |x|<k, 0.5 k² + k|x-k| otherwise
  • Derivative \phi(x) = x if |x|<k, k sgn(x) otherwise
  • Weight w(x) = \phi(x)/x = 1 if |x|<k, k/|x| otherwise
+ Inheritance diagram for gtsam::noiseModel::mEstimator::Huber:

Public Member Functions

 Huber (double k=1.345, const ReweightScheme reweight=Block)
 
double weight (double distance) const override
 This method is responsible for returning the weight function for a given amount of error.
 
double loss (double distance) const override
 This method is responsible for returning the total penalty for a given amount of error.
 
void print (const std::string &s) const override
 
bool equals (const Base &expected, double tol=1e-8) const override
 
double modelParameter () const
 
- Public Member Functions inherited from gtsam::noiseModel::mEstimator::Base
 Base (const ReweightScheme reweight=Block)
 
ReweightScheme reweightScheme () const
 Returns the reweight scheme, as explained in ReweightScheme.
 
double sqrtWeight (double distance) const
 
Vector weight (const Vector &error) const
 produce a weight vector according to an error vector and the implemented robust function
 
Vector sqrtWeight (const Vector &error) const
 square root version of the weight function
 
void reweight (Vector &error) const
 reweight block matrices and a vector according to their weight implementation
 
void reweight (std::vector< Matrix > &A, Vector &error) const
 
void reweight (Matrix &A, Vector &error) const
 
void reweight (Matrix &A1, Matrix &A2, Vector &error) const
 
void reweight (Matrix &A1, Matrix &A2, Matrix &A3, Vector &error) const
 

Static Public Member Functions

static shared_ptr Create (double k, const ReweightScheme reweight=Block)
 

Public Types

typedef boost::shared_ptr< Hubershared_ptr
 
- Public Types inherited from gtsam::noiseModel::mEstimator::Base
enum  ReweightScheme { Scalar , Block }
 the rows can be weighted independently according to the error or uniformly with the norm of the right hand side
 
typedef boost::shared_ptr< Baseshared_ptr
 

Protected Attributes

double k_
 
- Protected Attributes inherited from gtsam::noiseModel::mEstimator::Base
ReweightScheme reweight_
 Strategy for reweighting.
 

Friends

class boost::serialization::access
 Serialization function.
 

Member Function Documentation

◆ equals()

bool gtsam::noiseModel::mEstimator::Huber::equals ( const Base expected,
double  tol = 1e-8 
) const
overridevirtual

◆ loss()

double gtsam::noiseModel::mEstimator::Huber::loss ( double  distance) const
overridevirtual

This method is responsible for returning the total penalty for a given amount of error.

For example, this method is responsible for implementing the quadratic function for an L2 penalty, the absolute value function for an L1 penalty, etc.

TODO(mikebosse): When the loss function has as input the norm of the error vector, then it prevents implementations of asymmeric loss functions. It would be better for this function to accept the vector and internally call the norm if necessary.

This returns \rho(x) in mEstimator

Reimplemented from gtsam::noiseModel::mEstimator::Base.

◆ print()

void gtsam::noiseModel::mEstimator::Huber::print ( const std::string &  s = "") const
overridevirtual

◆ weight()

double gtsam::noiseModel::mEstimator::Huber::weight ( double  distance) const
overridevirtual

This method is responsible for returning the weight function for a given amount of error.

The weight function is related to the analytic derivative of the loss function. See https://members.loria.fr/MOBerger/Enseignement/Master2/Documents/ZhangIVC-97-01.pdf for details. This method is required when optimizing cost functions with robust penalties using iteratively re-weighted least squares.

This returns w(x) in mEstimator

Implements gtsam::noiseModel::mEstimator::Base.


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