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 () override | |
MinimumError | Update (const MinimumState &, const MinimumParameters &, const FunctionGradient &) const override |
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>
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inline |
Definition at line 50 of file FumiliErrorUpdator.h.
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inlineoverride |
Definition at line 52 of file FumiliErrorUpdator.h.
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overridevirtual |
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.
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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.