class TMinuitMinimizer: public ROOT::Math::Minimizer

 Constructor for TMinuitMinimier class via an enumeration specifying the minimization
 algorithm type. Supported types are : kMigrad, kSimplex, kCombined (a combined
 Migrad + Simplex minimization) and kMigradImproved (a Migrad mininimization folloed by an
 improved search for global minima). The default type is Migrad (kMigrad).

 constructor from a char * for the algorithm type, used by the plug-in manager
 The names supported (case unsensitive) are:
  Migrad (default), Simplex, Minimize (for the combined Migrad+ Simplex) and Migrad_imp

 Destructor implementation.

 Implementation of copy constructor (it is private).

 static method to control usage of global TMinuit instance

 Set the objective function to be minimized, by passing a function object implement the
 basic multi-dim Function interface. In this case the derivatives will be
 calculated by Minuit
 Here a TMinuit instance is created since only at this point we know the number of parameters

 Set the objective function to be minimized, by passing a function object implement the
 multi-dim gradient Function interface. In this case the function derivatives are provided
 by the user via this interface and there not calculated by Minuit.

 implementation of FCN static function used internally by TMinuit.
 Adapt IMultiGenFunction interface to TMinuit FCN static function

 implementation of FCN static function used internally by TMinuit.
 Adapt IMultiGradFunction interface to TMinuit FCN static function in the case of user
 provided gradient.

 set a free variable.

 set a limited variable.

 set a fixed variable.

 set the value of an existing variable
 parameter must exist or return false

 return the variable name

 return variable index

 perform the minimization using the algorithm chosen previously by the user
 By default Migrad is used.
 Return true if the found minimum is valid and update internal chached values of
 minimum values, errors and covariance matrix.
 Status of minimizer is set to:
 migradResult + 10*minosResult + 100*hesseResult + 1000*improveResult

 retrieve from TMinuit minimum parameter values
 and errors

 get covariance error matrix from TMinuit
 when some parameters are fixed filled the corresponding rows and column with zero's

 return total number of function calls

 return minimum function value

 return expected distance from the minimum

 return number of free parameters

 get covariance matrix

 get Hessian - inverse of covariance matrix
 just invert it
 but need to get the compact form to avoid the zero for the fixed parameters

 return status of covariance matrix
           status:  0= not calculated at all
                    1= approximation only, not accurate
                    2= full matrix, but forced positive-definite
                    3= full accurate covariance matrix

 global correlation coefficient for parameter i

 Perform Minos analysis for the given parameter  i

 reset TMinuit

 check if a parameter is defined and in case it was fixed released
 TMinuit is not able to release free parameters by redefining them
 so we need to force the release

 print-out results using classic Minuit format (mnprin)

 suppress Minuit2 warnings

 contour plot for parameter i and j
 need a valid FunctionMinimum otherwise exits

 scan a parameter (variable) around the minimum value
 the parameters must have been set before
 if xmin=0 && xmax == 0  by default scan around 2 sigma of the error
 if the errors  are also zero then scan from min and max of parameter range
 (if parameters are limited Minuit scan from min and max instead of 2 sigma by default)
 (force in that case to use errors)

 perform calculation of Hessian

 return  pointer to X values at the minimum

 return pointer to gradient values at the minimum

 this is <= Function().NDim() which is the total
 number of variables (free+ constrained ones)

 minimizer provides error and error matrix

 return errors at the minimum

 return covariance matrices elements
       if the variable is fixed the matrix is zero
       The ordering of the variables is the same as in errors