Description:

This 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) or Efficient Global Optimization (EGO).

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

Basically, it 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 use for the preliminary sampling step

• kernel parameters are trained with the preliminary samples and fixed afterwards to avoid bias and divergence

• matrix algebra tricks are used to guarantee that any covariance matrix remains SPD and reduce computational cost.

• 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 at: http://bitbucket.org/rmcantin/bayesian-optimization/wiki/Home

Changes to previous version:

Initial Announcement on mloss.org.

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

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