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arXiv · 1405.7430

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

Ruben Martinez-Cantin
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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.

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Ruben Martinez-Cantin. 2014-05-29. BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits. https://arxiv.org/abs/1405.7430

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