Handling crashes while optimizing

[oarcelus]

Which is the best strategy to handle a model crashing upon evaluation while optimizing input parameters?

[lindonroberts]

I assume you mean that the objective function you provide as an input to Py-BOBYQA sometimes crashes for particular parameter combinations? Py-BOBYQA doesn't have the ability to explicitly handle nan/error values, so the best option when a crash occurs would be to return a large function value (e.g. at least 1 order of magnitude larger than the objective value at nearby points). That way, Py-BOBYQA will never return these parameters as a solution, and it will try to search for a solution away from parameter combinations close to where the crash occurred.

[oarcelus]

In that case my objective function should have access to the solution history, how should I do that? I could access the logging string and parse the evaluations but that solution is subpar imho. Is there a better way?

[lindonroberts]

You can either just pick a moderately large value arbitrarily, from just observing your problem in a few runs. Otherwise, you can wrap your function in a class which saves the values you've seen, something like:

class FunctionWrapper:
    def __init__(self, objfun):
        self.objfun = objfun
        self.history = []

    def __call__(self, x):
        try:
            f = self.objfun(x)
            self.history.append(f)
        except:
            f = 10 * self.history[-1]
        return f

myfun = FunctionWrapper(original_objfun)
soln = pybobyqa.solve(myfun, x0, ...)