BayesOpt is an efficient implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design, stochastic bandits and hyperparameter tunning. In the literature it is also called Sequential Kriging Optimization (SKO), Sequential Model-Based Optimization (SMBO) or Efficient Global Optimization (EGO).
Bayesian optimization uses a distribution over functions to build a model of the unknown function for we are looking the extrema, and then apply some active learning strategy to select the query points that provides most potential interest or improvement. Thus, it is a sampling efficient method for nonlinear optimization, design of experiments or bandits-like problems.
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