BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Machine Learning
2014-05-30 v1
Abstract
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 is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for 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.
Cite
@article{arxiv.1405.7430,
title = {BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits},
author = {Ruben Martinez-Cantin},
journal= {arXiv preprint arXiv:1405.7430},
year = {2014}
}