class TLinearMinimizer: public ROOT::Math::Minimizer

 set the function to minimize

 set a function to minimize using gradient

 set a new free variable

 set a new fixed variable (override if minimizer supports them )

 method to perform the minimization

 return minimum function value

 return  pointer to X values at the minimum

 return expected distance reached from the minimum (re-implement if minimizer provides it

 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
 (re-implement if minimizer supports bounded parameters)

 minimizer provides error and error matrix

 return errors at the minimum

 return covariance matrices element for variables ivar,jvar
       if the variable is fixed the return value is zero
       The ordering of the variables is the same as in the parameter and errors vectors

return status of covariance matrix
 using Minuit convention {0 not calculated 1 approximated 2 made pos def , 3 accurate}
 Minimizer who implements covariance matrix calculation will re-implement the method