Great File, many thanks for it.
However, I am currently facing some issues with my function (bad scaled parameters), and therefore with the second derivatives matrix (Hessian).
Here is the deal: I am minimizing a cost function (xˆ2 = (y - ymodel)^2), where my model is 3 nonlinear parameter one, using a Nelder Mead algo.
Using the |hessian| function from your package using best-fit parameters, yielded me some strange results (since Parameter_1 ranges from [0.1:0.9], Parameter_2 from [10:30] and Parameter_3 from [150:250]), that is, I need to re-escale them. My question is, what is the best way to do that and does your code endogenously scale the results by any chance ?
Thank you very much in advance,