[class adsal] ADSal - adaptive diminishing smoothing algorithm Based on the paper: B. Savchynskyy, S. Schmidt, J. H. Kappes, C. Schnörr Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing, In UAI, 2012, pp. 746-755 More...

#include <trws_adsal.hxx>

Inheritance diagram for opengm::ADSal< GM, ACC >:

Collaboration diagram for opengm::ADSal< GM, ACC >:

Public Types
typedef trws_base::SmoothingBasedInference< GM, ACC >	parent

typedef ACC	AccumulationType

typedef GM	GraphicalModelType

typedef parent::Storage	Storage

typedef parent::SumProdSolver	SumProdSolver

typedef parent::MaxSumSolver	MaxSumSolver

typedef parent::PrimalBoundEstimator	PrimalBoundEstimator

typedef ADSal_Parameter< GM >	Parameter

typedef visitors::VerboseVisitor< ADSal< GM, ACC > >	VerboseVisitorType

typedef visitors::TimingVisitor< ADSal< GM, ACC > >	TimingVisitorType

typedef visitors::EmptyVisitor< ADSal< GM, ACC > >	EmptyVisitorType

Public Types inherited from opengm::trws_base::SmoothingBasedInference< GM, ACC >
typedef Inference< GM, ACC >	parent

typedef ACC	AccumulationType

typedef GM	GraphicalModelType

typedef trws_base::DecompositionStorage< GM >	Storage

typedef trws_base::SumProdTRWS< GM, ACC >	SumProdSolver

typedef trws_base::MaxSumTRWS< GM, ACC >	MaxSumSolver

typedef PrimalLPBound< GM, ACC >	PrimalBoundEstimator

typedef TRWS_Reparametrizer< Storage, ACC >	ReparametrizerType

typedef SmoothingStrategy< GM, ACC >	SmoothingStrategyType

typedef SmoothingBasedInference_Parameter< GM >	Parameter

Public Types inherited from opengm::Inference< GM, ACC >
typedef GM	GraphicalModelType

typedef ACC	AccumulationType

typedef GraphicalModelType::LabelType	LabelType

typedef GraphicalModelType::IndexType	IndexType

typedef GraphicalModelType::ValueType	ValueType

typedef GraphicalModelType::OperatorType	OperatorType

typedef GraphicalModelType::FactorType	FactorType

typedef GraphicalModelType::IndependentFactorType	IndependentFactorType

typedef GraphicalModelType::FunctionIdentifier	FunctionIdentifier

Public Member Functions
	ADSal (const GraphicalModelType &gm, const Parameter &param)

std::string	name () const

InferenceTermination	infer ()

template<class VISITOR >
InferenceTermination	infer (VISITOR &visitor)

InferenceTermination	oldinfer ()

Public Member Functions inherited from opengm::trws_base::SmoothingBasedInference< GM, ACC >
	SmoothingBasedInference (const GraphicalModelType &gm, const Parameter &param)

virtual	~SmoothingBasedInference ()

InferenceTermination	arg (std::vector< LabelType > &out, const size_t=1) const
	output a solution More...

const GraphicalModelType &	graphicalModel () const

ValueType	bound () const
	return a bound on the solution More...

ValueType	value () const
	return the solution (value) More...

ValueType	UpdateSmoothDualEstimates (ValueType smoothingValue, ValueType *pderivativeValue)

void	getTreeAgreement (std::vector< bool > &out, std::vector< LabelType > plabeling=0, std::vector< std::vector< LabelType > > ptreeLabelings=0)

Storage &	getDecompositionStorage ()

const MaxSumSolver::FactorProperties &	getFactorProperties () const

ReparametrizerType *	getReparametrizer (const typename ReparametrizerType::Parameter &params=typename ReparametrizerType::Parameter())

InferenceTermination	marginal (const IndexType varID, IndependentFactorType &out)

template<class VISITOR >
opengm::InferenceTermination	_Presolve (VISITOR &visitor, size_t *piterCounter)

Public Member Functions inherited from opengm::Inference< GM, ACC >
virtual	~Inference ()

virtual void	setStartingPoint (typename std::vector< LabelType >::const_iterator)
	set initial labeling More...

virtual InferenceTermination	args (std::vector< std::vector< LabelType > > &) const

virtual InferenceTermination	marginal (const size_t, IndependentFactorType &) const
	output a solution for a marginal for a specific variable More...

virtual InferenceTermination	factorMarginal (const size_t, IndependentFactorType &) const
	output a solution for a marginal for all variables connected to a factor More...

InferenceTermination	constrainedOptimum (std::vector< IndexType > &, std::vector< LabelType > &, std::vector< LabelType > &) const

InferenceTermination	modeFromMarginal (std::vector< LabelType > &) const

InferenceTermination	modeFromFactorMarginal (std::vector< LabelType > &) const

Public Attributes
	OPENGM_GM_TYPE_TYPEDEFS

Public Attributes inherited from opengm::trws_base::SmoothingBasedInference< GM, ACC >
	OPENGM_GM_TYPE_TYPEDEFS

Additional Inherited Members
Protected Member Functions inherited from opengm::trws_base::SmoothingBasedInference< GM, ACC >
template<class VISITOR >
InferenceTermination	_Presolve (VISITOR &visitor, size_t *piterCounter=0)

template<class VISITOR >
size_t	_EstimateStartingSmoothing (VISITOR &visitor)

bool	_UpdateSmoothing (ValueType primalBound, ValueType dualBound, ValueType smoothDualBound, ValueType derivativeValue, size_t iterationCounterPlus1=0)
	returns number of oracle calls; More...

ValueType	_EstimateRhoDerivative () const

ValueType	_FastEstimateRhoDerivative () const

void	_UpdatePrimalEstimator ()

void	_SelectOptimalBoundsAndLabeling ()

bool	_isLPBoundComputed () const

bool	_CheckStoppingCondition (InferenceTermination *)

Protected Attributes inherited from opengm::trws_base::SmoothingBasedInference< GM, ACC >
Parameter	_parameters

Storage	_storage

SumProdSolver	_sumprodsolver

MaxSumSolver	_maxsumsolver

PrimalBoundEstimator	_estimator

ValueType	_bestPrimalLPbound

ValueType	_bestPrimalBound

ValueType	_bestDualBound

ValueType	_bestIntegerBound

std::vector< LabelType >	_bestIntegerLabeling

SmoothingStrategyType *	psmoothingStrategy

SumProdSolver::OutputContainerType	_marginalsTemp

Detailed Description

template<class GM, class ACC>
class opengm::ADSal< GM, ACC >

[class adsal] ADSal - adaptive diminishing smoothing algorithm Based on the paper: B. Savchynskyy, S. Schmidt, J. H. Kappes, C. Schnörr Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing, In UAI, 2012, pp. 746-755

it provides:

primal integer approximate solution for MRF energy minimization problem
approximate primal and dual solutions of the local polytope relaxation of the problem. Duality gap converges to zero in the limit and can be used as an accuracy measure of the algorithm.

TODO: Code can be significantly speeded up!

Corresponding author: Bogdan Savchynskyy

Definition at line 97 of file trws_adsal.hxx.