EndCriteria Class Reference
#include <ql/math/optimization/endcriteria.hpp>
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:
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() (const Size iteration, Size &statState, const bool positiveOptimization, const Real fold, const Real normgold, const Real fnew, const Real normgnew, EndCriteria::Type &ecType) const |
| bool | checkMaxIterations (const Size iteration, EndCriteria::Type &ecType) const |
| bool | checkStationaryPoint (const Real xOld, const Real xNew, Size &statStateIterations, EndCriteria::Type &ecType) const |
| bool | checkStationaryFunctionValue (const Real fxOld, const Real fxNew, Size &statStateIterations, EndCriteria::Type &ecType) const |
| bool | checkStationaryFunctionAccuracy (const Real f, const bool positiveOptimization, EndCriteria::Type &ecType) const |
| bool | checkZeroGradientNorm (const 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_ |
Member Function Documentation
| bool operator() | ( | const Size | iteration, | |
| Size & | statState, | |||
| const bool | positiveOptimization, | |||
| const Real | fold, | |||
| const Real | normgold, | |||
| const Real | fnew, | |||
| const Real | normgnew, | |||
| EndCriteria::Type & | ecType | |||
| ) | const |
Test if the number of iterations is not too big and if a minimum point is not reached
| bool checkMaxIterations | ( | const Size | iteration, | |
| EndCriteria::Type & | ecType | |||
| ) | const |
Test if the number of iteration is below MaxIterations
| bool checkStationaryPoint | ( | const Real | xOld, | |
| const Real | xNew, | |||
| Size & | statStateIterations, | |||
| EndCriteria::Type & | ecType | |||
| ) | const |
Test if the root variation is below rootEpsilon
| bool checkStationaryFunctionValue | ( | const Real | fxOld, | |
| const Real | fxNew, | |||
| Size & | statStateIterations, | |||
| EndCriteria::Type & | ecType | |||
| ) | const |
Test if the function variation is below functionEpsilon
| bool checkStationaryFunctionAccuracy | ( | const Real | f, | |
| const bool | positiveOptimization, | |||
| EndCriteria::Type & | ecType | |||
| ) | const |
Test if the function value is below functionEpsilon
| bool checkZeroGradientNorm | ( | const Real | gNorm, | |
| EndCriteria::Type & | ecType | |||
| ) | const |
Test if the gradient norm variation is below gradientNormEpsilon
Test if the gradient norm value is below gradientNormEpsilon