In the case of the Fumili algorithm the Error matrix (or the Hessian matrix containing the (approximate) second derivatives) is calculated using a linearization of the model function negleting second derivatives.
(In some sense the Name Updator is a little bit misleading as the Error matrix is not calculated by iteratively updating, like in Davidon's or other similar variable metric methods, but by recalculating each time).
Definition at line 47 of file FumiliErrorUpdator.h.
Public Member Functions  
FumiliErrorUpdator ()  
~FumiliErrorUpdator ()  
virtual MinimumError  Update (const MinimumState &, const MinimumParameters &, const FunctionGradient &) const 
Member function which is only present due to the design already in place of the software.  
virtual MinimumError  Update (const MinimumState &fMinimumState, const MinimumParameters &fMinimumParameters, const GradientCalculator &fGradientCalculator, double lambda) const 
Member function that calculates the Error matrix (or the Hessian matrix containing the (approximate) second derivatives) using a linearization of the model function negleting second derivatives.  
Public Member Functions inherited from ROOT::Minuit2::MinimumErrorUpdator  
virtual  ~MinimumErrorUpdator () 
#include <Minuit2/FumiliErrorUpdator.h>

inline 
Definition at line 50 of file FumiliErrorUpdator.h.

inline 
Definition at line 52 of file FumiliErrorUpdator.h.

virtual 
Member function which is only present due to the design already in place of the software.
As all classes calculating the Error matrix are supposed inherit from the MinimumErrorUpdator they must inherit this method. In some methods calculating the aforementioned matrix some of these parameters are not needed and other parameters are necessary... Hopefully, a more elegant solution will be found in the future.
Implements ROOT::Minuit2::MinimumErrorUpdator.
Definition at line 32 of file FumiliErrorUpdator.cxx.

virtual 
Member function that calculates the Error matrix (or the Hessian matrix containing the (approximate) second derivatives) using a linearization of the model function negleting second derivatives.
fMinimumState  used to calculate the change in the covariance matrix between the two iterations 
fMinimumParameters  the parameters at the present iteration 
fGradientCalculator  the Gradient calculator used to retrieved the Parameter transformation 
lambda  the Marquard lambda factor 
Definition at line 43 of file FumiliErrorUpdator.cxx.