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I was looking at the scipy.minimize documentation to see if I could find a way to terminate optimization when the loss gets below some cut-off, and I couldn't see an obvious way to do it.
The callback option only lets me pass it the xk, but I need to pass additional parameters to evaluate my loss.
Any thoughts on a straightforward way to do this?
So I went ahead and just modified the scipy optimize.py source to do what I wanted. I assume this is not best practices, but... moving on. So I used the inspect module to figure out where the corresponding functions are. So for the Powell optimization routine, I found:
def _minimize_powell
on line 2509 of scipy/optimize/optimize.py. Then I went to line 2593, where I noticed a couple of termination conditions, and I added my own termination condition:
if fval < 0.1:
break
which now terminates optimization and returns the optimization struct as desired when this condition is met.
