Blackbox function optimization with bounds?
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NeutralKaon commented
Hi there! Thanks for making this – it is incredibly impressive.
Some of the optimisers present natively support bounds (e.g. humpday/optimizers/freelunchcube.py
contains on line 22 bounds = np.array( [ np.array([0, 1])] * n_dim )
). My problem is bounded and rapidly becomes flat / infinite outside of a feasible region.
Is it possible, feasible, or even a remotely good idea to naïvely ask about the possibility of including lb
and ub
in recommend()
?