I would like to know whether constrained blackbox optimization is possible using mystic in python. If so what algorithms will be available in this optimization package?
Can mystic solve black box optimization problems with constraints?
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I'm the author of
mystic
. Yes, blackbox constrained optimization is possible in mystic. To see more about it, look at the docs here: https://github.com/uqfoundation/mystic, and in the docs links therein.In terms of constrained optimization, there's about 50 examples in the repo: https://github.com/uqfoundation/mystic/tree/master/examples2. Constraints can be symbolic or functional, equality and inequality, hard or soft, can be combined with and, or, not, and can be applied to any of the optimizers. Mystic's constraints are also portable, and can be applied to other optimization codes, like
scipy.optimize
, as well as machine learning codes likesklearn
.Mystic does not have a lot of optimizers, but the optimizers are very very customizable, and allows you to tweak almost every aspect of the optimization algorithm. Several more optimizers are for the most part developed, and will be added in a release this summer/fall.
Here's an explicit example from the above link: