Limbo: A Fast and Flexible Library for Bayesian Optimization
This provides a practical tool for domains like embedded systems or robotics where runtime efficiency is critical, though it is incremental as it builds on existing Bayesian optimization methods.
The authors tackled the problem of optimizing expensive, gradient-unknown functions by developing Limbo, a fast and flexible C++ library for Bayesian optimization, achieving about 2 times faster performance than BayesOpt with similar accuracy in benchmarks.
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.