Limbo: A Fast and Flexible Library for Bayesian Optimization

Abstract

Limbo is an open-source C++11 library for Bayesian optimization which is designed to be both highly flexible and very fast. It can be used to optimize functions for which the gradient is unknown, evaluations are expensive, and runtime cost matters (e.g., on embedded systems or robots). Benchmarks on standard functions show that Limbo is about 2 times faster than BayesOpt (another C++ library) for a similar accuracy.

References in corpus (2)

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

Cited by in corpus (1)

• Safety-Aware Robot Damage Recovery Using Constrained Bayesian Optimization and Simulated Priors