Source

BayesOpt / README.md

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:

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

The install guide for Windows, Linux and MacOS:
- [Install guide](http://rmcantin.bitbucket.org/html/install.html) or \ref install

For a complete description of supported systems:
- [Supported OS, compilers, versions...](https://bitbucket.org/rmcantin/bayesopt/wiki/Compatibility)


Using BayesOpt
--------------

If you just want to use BayesOpt as a library for nonlinear optimization:
- [Reference manual](http://rmcantin.bitbucket.org/html/reference.html) or \ref reference
- [Demos and examples](http://rmcantin.bitbucket.org/html/demos.html) or \ref demos

If you want to understand what is Bayesian optimization:
- [Bayesian optimization](http://rmcantin.bitbucket.org/html/bopttheory.html) or \ref bopttheory
- [Models and functions](http://rmcantin.bitbucket.org/html/modelopt.html) or \ref modelopt


Getting involved
----------------

The best place to ask questions and discuss about BayesOpt is the [bayesopt-discussion mailing list](https://groups.google.com/forum/#!forum/bayesopt-discussion). 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](http://bitbucket.org/rmcantin/bayesopt/wiki/Home) or subscribe
to the [bayesopt-discussion mailing
list](https://groups.google.com/forum/#!forum/bayesopt-discussion).


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.


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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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