QuantLib 0.3.3 http://quantlib.org User manual Introduction to QuantLib Introduction Project overview Where to get QuantLib Installation Usage Supported platforms Version history Todo list Additional resources The QuantLib group Copyright and license QuantLib components Core classes Date and time calculations Lattice methods The finite differences framework The Monte Carlo framework Short-rate models Currencies and FX rates Instruments and pricers Math tools Design patterns Term structures Utilities Examples Reference manual Modules Namespace List Class Hierarchy Compound List File List Namespace Members Compound Members File Members Unstable Features Deprecated List

Method Class Reference #include <method.hpp> Inheritance diagram for Method: [legend]List of all members. Detailed Description Optimization Method abstract class for constrained optimization pb. Public Member Functions void  setInitialValue (const Array &initialValue)   Set initial value. void  setEndCriteria (const EndCriteria &endCriteria)   Set optimization end criteria. int &  iterationNumber () const   current iteration number EndCriteria &  endCriteria () const   optimization end criteria int &  functionEvaluation () const   number of evaluation of cost function int &  gradientEvaluation () const   number of evaluation of cost function gradient double &  functionValue () const   value of cost function double &  gradientNormValue () const   value of cost function gradient norm Array &  x () const   current value of the local minimum Array &  searchDirection () const   current value of the search direction virtual void  minimize (const Problem &P) const=0   minimize the optimization problem P Protected Attributes Array  initialValue_   initial value of unknowns int  iterationNumber_   current iteration step in the Optimization process EndCriteria  endCriteria_   optimization end criteria int  functionEvaluation_   number of evaluation of cost function and its gradient int  gradientEvaluation_   number of evaluation of cost function and its gradient double  functionValue_   function and gradient norm values of the last step double  squaredNorm_   function and gradient norm values of the last step Array  x_   current values of the local minimum and the search direction Array  searchDirection_   current values of the local minimum and the search direction The documentation for this class was generated from the following file: ql/Optimization/method.hpp

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