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microprediction / humpday

Elo ratings for global black box derivative-free optimizers
MIT License
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Blackbox function optimization with bounds? #32

Open NeutralKaon opened 7 months ago

NeutralKaon commented 7 months ago

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()?

microprediction commented 1 month ago

Sorry for delayed response. In this benchmark the bounds are always the hypercube. So rather than lb, ub you need to transform the objective