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PyBADS is a Python implementation of the Bayesian Adaptive Direct Search(BADS) algorithm for fast and robust black-box optimization (Acerbi and Ma2017). BADS is an optimization algorithm designed to efficiently solvedifficult optimization problems where the objective function is rough(non-convex, non-smooth), mildly expensive (e.g., the function evaluationrequires more than 0.1 seconds), possibly noisy, and gradient information isunavailable. With BADS, these issues are well addressed, making it an excellentchoice for fitting computational models using methods such asmaximum-likelihood estimation. The algorithm scales efficiently to black-boxfunctions with up to $D \approx 20$ continuous input parameters and supportsbounds or no constraints. PyBADS comes along with an easy-to-use Pythonicinterface for running the algorithm and inspecting its results. PyBADS onlyrequires the user to provide a Python function for evaluating the targetfunction, and optionally other constraints.  Extensive benchmarks on both artificial test problems and large realmodel-fitting problems models drawn from cognitive, behavioral andcomputational neuroscience, show that BADS performs on par with or better thanmany other common and state-of-the-art optimizers (Acerbi and Ma 2017), makingit a general model-fitting tool which provides fast and robust solutions.

PyBADS: Fast and robust black-box optimization in Python