  PyBADS is a Python implementation of the Bayesian Adaptive Direct Search
(BADS) algorithm for fast and robust black-box optimization (Acerbi and Ma
2017). BADS is an optimization algorithm designed to efficiently solve
difficult optimization problems where the objective function is rough
(non-convex, non-smooth), mildly expensive (e.g., the function evaluation
requires more than 0.1 seconds), possibly noisy, and gradient information is
unavailable. With BADS, these issues are well addressed, making it an excellent
choice for fitting computational models using methods such as
maximum-likelihood estimation. The algorithm scales efficiently to black-box
functions with up to $D \approx 20$ continuous input parameters and supports
bounds or no constraints. PyBADS comes along with an easy-to-use Pythonic
interface for running the algorithm and inspecting its results. PyBADS only
requires the user to provide a Python function for evaluating the target
function, and optionally other constraints.
  Extensive benchmarks on both artificial test problems and large real
model-fitting problems models drawn from cognitive, behavioral and
computational neuroscience, show that BADS performs on par with or better than
many other common and state-of-the-art optimizers (Acerbi and Ma 2017), making
it a general model-fitting tool which provides fast and robust solutions.
