BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
This provides a tool for researchers and practitioners needing efficient optimization in various domains, but it is incremental as it packages existing methods into a library.
The authors developed BayesOpt, a library implementing state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits, and sequential experimental design problems, with the library being built in standard C++ for efficiency and offering interfaces for multiple programming languages.
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.