BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits. 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 characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of 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.
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References in zbMATH (referenced in 1 article )
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- Martinez-Cantin, Ruben: BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014)
Further publications can be found at: http://rmcantin.bitbucket.org/html/citelist.html