Criteria to end optimization process: More...

#include <ql/math/optimization/endcriteria.hpp>

Public Types
enum	Type { None, MaxIterations, StationaryPoint, StationaryFunctionValue, StationaryFunctionAccuracy, ZeroGradientNorm, Unknown }

Public Member Functions
	EndCriteria (Size maxIterations, Size maxStationaryStateIterations, Real rootEpsilon, Real functionEpsilon, Real gradientNormEpsilon)
	Initialization constructor.

Size	maxIterations () const

Size	maxStationaryStateIterations () const

Real	rootEpsilon () const

Real	functionEpsilon () const

Real	gradientNormEpsilon () const

bool	operator() (Size iteration, Size &statState, bool positiveOptimization, Real fold, Real normgold, Real fnew, Real normgnew, EndCriteria::Type &ecType) const

bool	checkMaxIterations (Size iteration, EndCriteria::Type &ecType) const

bool	checkStationaryPoint (Real xOld, Real xNew, Size &statStateIterations, EndCriteria::Type &ecType) const

bool	checkStationaryFunctionValue (Real fxOld, Real fxNew, Size &statStateIterations, EndCriteria::Type &ecType) const

bool	checkStationaryFunctionAccuracy (Real f, bool positiveOptimization, EndCriteria::Type &ecType) const

bool	checkZeroGradientNorm (Real gNorm, EndCriteria::Type &ecType) const

Protected Attributes
Size	maxIterations_
	Maximum number of iterations.

Size	maxStationaryStateIterations_
	Maximun number of iterations in stationary state.

Real	rootEpsilon_
	root, function and gradient epsilons

Real	functionEpsilon_

Real	gradientNormEpsilon_

Detailed Description

Criteria to end optimization process:

maximum number of iterations AND minimum number of iterations around stationary point
x (independent variable) stationary point
y=f(x) (dependent variable) stationary point
stationary gradient

Examples: BermudanSwaption.cpp, Gaussian1dModels.cpp, and GlobalOptimizer.cpp.

Member Function Documentation

Test if the number of iterations is not too big and if a minimum point is not reached

Test if the number of iteration is below MaxIterations

Test if the root variation is below rootEpsilon

bool checkStationaryFunctionValue	(	Real	fxOld,
		Real	fxNew,
		Size &	statStateIterations,
		EndCriteria::Type &	ecType
	)		const

Test if the function variation is below functionEpsilon

bool checkStationaryFunctionAccuracy	(	Real	f,
		bool	positiveOptimization,
		EndCriteria::Type &	ecType
	)		const

Test if the function value is below functionEpsilon

bool checkZeroGradientNorm	(	Real	gNorm,
		EndCriteria::Type &	ecType
	)		const

Test if the gradient norm variation is below gradientNormEpsilon

Test if the gradient norm value is below gradientNormEpsilon