Bayesian-Optimization /

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BayesOpt: A Bayesian optimization toolbox {#mainpage}

BayesOpt is an free, efficient, implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO).

The online HTML version of these docs:

Bayesian optimization uses a distribution over functions to build a metamodel of the unknown function for we are looking the extrema, and then apply some active learning strategy to select the query points that provides most potential interest for the seek. For that reason, it has been traditionally intended for optimization of expensive function. However, the efficiency of the library make it also interesting for many types of functions.

It is intended to be both fast and clear for development and research. At the same time, it does everything the "right way". For example:

  • latin hypercube sampling is used for the preliminary design step,
  • extensive use of Cholesky decomposition and related tecniques to improve numeral stability and reduce computational cost,
  • kernels, criteria and parametric functions can be combined to produce more advanced functions,
  • etc.

The documentation can be found in:

You can also find more details at the proyect wiki or subscribe to the bayesopt-discussion mailing list.

Important: This code is free to use. However, if you are using the library, specially if it is for research or academic purposes, please send me an email at with your name, institution and a brief description of your interest for this code (one or two lines).

If you use BayesOpt in a work that leads to a scientific publication, we would appreciate it if you would kindly cite BayesOpt in your manuscript. Cite BayesOpt as something like:

Ruben Martinez-Cantin, BayesOpt: a toolbox for nonlinear-optimization, experimental design and stochastic bandits,

Copyright (C) 2011-2013 Ruben Martinez-Cantin

BayesOpt is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

BayesOpti is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with BayesOpt. If not, see <>;.