BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

TL;DR

BayesOpt is a versatile, efficient C++ library implementing advanced Bayesian optimization techniques for nonlinear problems, stochastic bandits, and experimental design, with multi-language support.

Contribution

It introduces a portable, flexible library with state-of-the-art Bayesian optimization algorithms accessible via multiple programming languages.

Findings

High sample efficiency in nonlinear optimization tasks

Supports multiple languages including C, C++, Python, Matlab, Octave

Demonstrates effectiveness in experimental design and bandit problems

Abstract

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.