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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: http://rmcantin.bitbucket.org/html/

Bayesian optimization uses a distribution over functions to build a model 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 or improvement. Thus, it is a sampling efficient method for nonlinear optimization, design of experiments or bandits-like problems.

Getting and installing BayesOpt

The library can be download from any of this sources:

The install guide for Windows, Linux and MacOS: - Install guide or \ref install

For a complete description of supported systems: - Supported OS, compilers, versions...

Using BayesOpt

If you just want to use BayesOpt as a library for nonlinear optimization: - Reference manual or \ref reference - Demos and examples or \ref demos

If you want to understand what is Bayesian optimization: - Bayesian optimization or \ref bopttheory - Models and functions or \ref modelopt

Getting involved

The best place to ask questions and discuss about BayesOpt is the bayesopt-discussion mailing list. Alternatively, you may directly contact Ruben Martinez-Cantin rmcantin@unizar.es.

Please file bug reports at: https://bitbucket.org/rmcantin/bayesopt/issues

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

Using BayesOpt for academic or commercial purposes

This code is licensed under the GPL and it is free to use. However, please consider these recomentations when using BayesOpt:

  • If you are using the library for research or academic purposes, please send me an email at rmcantin@unizar.es 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. If you use a specific algorithm, please also cite the corresponding work. The reference for each specific algorithm can be found in the documentation. Cite BayesOpt as something like:

Ruben Martinez-Cantin, BayesOpt: a toolbox for nonlinear-optimization, experimental design and stochastic bandits, http://bitbucket.org/rmcantin/bayesopt

Commercial applications may also adquire a commercial license which allows more flexible terms than GPL. Please contact rmcantin@unizar.es for details.


Copyright (C) 2011-2013 Ruben Martinez-Cantin rmcantin@unizar.es

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 <http:www.gnu.org/licenses/>.


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