Dear experts
I wonder what is the initial step length of a parameter? When I set the step of a parameter to 1. by fFit->SetVariable(0, "lambda", lambda, 1.);, then I found the first step length is not 1 but roughly 0.01, why?
Hello @Crisps,
Reading a bit of code, I would say that Minuit2 interprets the last arguments as the parameter error. At least the authors refer to that argument with that word.
I also added printouts to the NumericalMinimization tutorial, and can confirm that the first gradient computation happens with 1/100 of the parameter error, but the steps afterwards seem to be what I asked.
To compute the initial gradient, it would be reasonable for Minuit2 to not use the full error as a step size, but to use a fraction of that to “carefully” start the fit. Here is how it’s computing the first gradient where I asked for step size [0.2, 0.2]:
-1 1.2
-0.998 1.2
-1.002 1.2
-0.9998 1.2
-1.0002 1.2
-1 1.202
-1 1.198
Afterwards, when it’s taking the first step, it seems to actually step by 0.2 for the second parameter:
-1.10526 1
Note that the gradient is roughly [2, 1], so the step for the first parameter is only half the step size of the second. After that, the error estimation for the parameters happens internally anyway, so the initial step sizes will be adapted to the behaviour of the function.
Is that enough to understand your case or should we call in a Minuit expert?
Thank you for your explanation, in my case because the initial value is staying near some local minimum so I need the minimizer to search wider to avoid that local minimum, should I enlarge the step or?
I also attach the plot of scanning along the parameter, you can see there is a (not so deep but wide) local minimum near 0
