Project details for BayesOpt, a Bayesian Optimization toolbox

Screenshot BayesOpt, a Bayesian Optimization toolbox 0.7.1

by rmcantin - July 3, 2014, 00:30:50 CET [ Project Homepage BibTeX Download ]

view ( today), download ( today ), 0 subscriptions


BayesOpt is an efficient, C++ 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), Sequential Model Based Optimization (SMBO) or Efficient Global Optimization (EGO).

There are also interfaces for C, Matlab/Octave and Python. The online HTML version of the documentation is in:

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:

  • there are different methods that can be used for the preliminary design step,
  • extensive use of Cholesky decomposition and related techniques to improve numeral stability and reduce computational cost,
  • kernels, criteria and parametric functions can be combined to produce more advanced functions, etc.

Originally, it was developed for as part of a robotics research project, where a Gaussian process with hyperpriors on the mean and signal covariance parameters. Then, the metamodel was constructed using the Maximum a Posteriory (MAP) of the parameters. However, the library now has grown to support many more surrogate models, with different distributions (Gaussian processes, Student's-t processes, etc.), with many kernels and mean functions. It also provides different criteria (even some combined criteria) so the library can be used to any problem involving some bounded optimization, stochastic bandits, active learning for regression, etc.

You can also find more details in the project webpage:

Changes to previous version:

-Added MI criterion

-Simplified Python install

-Fixed bugs in annealed criteria

BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Macos
Data Formats: Agnostic
Tags: Active Learning, Optimization, Experimental Design, Gaussian Process, Bandits, Bayesian Optimization
Archive: download here


No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.