juancamilog/bayesopt

BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.

BayesOpt: A Bayesian optimization library

BayesOpt is an efficient implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design, stochastic bandits and hyperparameter tunning. In the literature it is also called Sequential Kriging Optimization (SKO), Sequential Model-Based Optimization (SMBO) 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:

• Download: https://bitbucket.org/rmcantin/bayesopt
• Mirror: https://github.com/rmcantin/bayesopt
• Mirror: http://mloss.org/software/view/453/

The install guide and documentation for Windows, Linux and MacOS:

• Online install guide or [Local install guide](@ref install)

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 [email protected].

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

Using BayesOpt for academic or commercial purposes

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

• 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:

Ruben Martinez-Cantin, BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits. Journal of Machine Learning Research, 2014

• In addition, if you use a specific algorithm (REMBO, GP-Hedge, etc.), please also cite the corresponding work. The reference for each specific algorithm can be found in the documentation.

• If you are using the library for research or academic purposes or to build free software, please send an email to [email protected] with a brief description or link to your interest for this code (one or two lines). There will be a section with links to research/papers/software that use BayesOpt.

• Commercial applications may also adquire a commercial license. Please contact [email protected] for details.

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Copyright (C) 2011-2014 Ruben Martinez-Cantin [email protected]

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.

BayesOpt 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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