BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Year: 2014, Volume: 15, Issue: 115, Pages: 3915−3919
BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.