Support dependencies between optimization parameters

It would be great if we could enter dependencies between parameters for optimization. For example, if I have the parameter bounds:

pbounds = {'a': (0, 5),
           'b': (0, 10)}

and know that for my application, no meaningful results will come from parameter choices where b < a, I would like to be able to force the optimizer to only choose samples such that b >= a.

See [M. T. M. Emmerich et. al. 2008] for some discussion of this. That’s just about the only paper that tries it that I can find so far.

[fmfn]

I’ll take a look at the PR. But one idea that sometimes can be used is to parametrize your parameters differently, making the dependency more explicit:

instead of pbounds = {'a': (0, 5), 'b': (0, 10} with the condition b < a, you could change coordinates to: x = a and y = b - a, and define pbounds = {'x': (0, 5), 'y': (0, 5)}, and call your function like so: f(a=x, b=x + y).

[bwheelz36]

addressed in #344