How to change scipy curve_fit/least_squares step size?

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I have a python function that takes a bunch (1 or 2) of arguments and returns a 2D array. I have been trying to use scipy curve_fit and least_squares to optimize the input arguments so that the resultant 2D array matches another 2D array that has be pre-made. I ran into the problem of both the methods returning me the initial guess as the converged solution. After ripping apart much hair from my head, I have figured out that the issue was that since the small increment that it makes to the initial guess is too small to make any difference in the 2D array that my function returns (as the cell values in the array are quantized and are not continuous) and hence scipy assumes that it has reached convergence (or local minimum) at the initial guess.

I was wondering if there is a way around this (such as forcing it to use a bigger increment while guessing). Thanks.

least-squares curve-fitting scipy python
1 Answers

I have ran into a very similar problem recently and it turns out that these kind of optimizers work only for continous-differentiable functions. That's why they would return the initial parameters, as the function you want to fit cannot be differentiated. In my case, I could manually make my fit function differentiable by first fitting a polynomial function to it before plugging it into the curve_fit optimizer.

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