pySOT is a collection of synchronous and asynchronous surrogate optimization strategies, implemented in the POAP framework (https://github.com/dbindel/POAP). We support the stochastic RBF method by Regis and Shoemaker along with various extensions of this method and a general surrogate optimization strategy which covers most Bayesian optimization methods. We have implemented many different surrogate models, experimental designs, auxiliary problems (also known as acquisition functions), and a large set of test problems. The object-oriented design makes it easy to add new surrogate models, experimental designs, and auxiliary problems and use them within the framework.
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- Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)