class TMinuitMinimizer: public ROOT::Math::Minimizer

      Default constructor

      Constructor from a char * (used by PM)

      Destructor (no operations)

 usually copying is non trivial, so we make this unaccessible

      Copy constructor

 set the function to minimize

 set the function to minimize

 set free variable

 set upper/lower limited variable (override if minimizer supports them )

 set lower limit variable  (override if minimizer supports them )

 set upper limit variable (override if minimizer supports them )

 set fixed variable (override if minimizer supports them )

 set the value of an existing variable

 set the step size of an existing variable

 set the lower-limit of an existing variable

 set the upper-limit of an existing variable

 set the limits of an existing variable

 fix an existing variable

 release an existing variable

 query if an existing variable is fixed (i.e. considered constant in the minimization)
 note that by default all variables are not fixed

 get variable settings in a variable object (like ROOT::Fit::ParamsSettings)

 method to perform the minimization

 return minimum function value

 return expected distance reached from the minimum

 return  pointer to X values at the minimum

 return pointer to gradient values at the minimum

 number of function calls to reach the minimum

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

 number of free variables (real dimension of the problem)
 this is <= Function().NDim() which is the total

 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

       Fill the passed array with the  covariance matrix elements
       if the variable is fixed or const the value is zero.
       The array will be filled as cov[i *ndim + j]
       The ordering of the variables is the same as in errors and parameter value.
       This is different from the direct interface of Minuit2 or TMinuit where the
       values were obtained only to variable parameters

       Fill the passed array with the Hessian matrix elements
       The Hessian matrix is the matrix of the second derivatives
       and is the inverse of the covariance matrix
       If the variable is fixed or const the values for that variables are zero.
       The array will be filled as h[i *ndim + j]

return status of covariance matrix

global correlation coefficient for variable i

 minos error for variable i, return false if Minos failed

      perform a full calculation of the Hessian matrix for error calculation

      scan a parameter i around the minimum. A minimization must have been done before,
      return false if it is not the case

      find the contour points (xi,xj) of the function for parameter i and j around the minimum
      The contour will be find for value of the function = Min + ErrorUp();

 return reference to the objective function
virtual const ROOT::Math::IGenFunction & Function() const;
 get name of variables (override if minimizer support storing of variable names)

 get index of variable given a variable given a name
 return always -1 . (It is Not implemented)

 static function to switch on/off usage of static global TMinuit instance (gMinuit)
 By default it is used (i.e. is on). Method returns the previous state

 suppress the minuit warnings (if called with false will enable them)
 By default they are suppressed only when the printlevel is <= 0

 implementation of FCN for Minuit

 implementation of FCN for Minuit when user provided gradient is used

 initialize the TMinuit instance

 reset

release a parameter that is fixed  when it is redefined

 retrieve minimum parameters and errors from TMinuit

 retrieve error matrix from TMinuit

 check TMinuit instance

check parameter