Entropy Search for Information-Efficient Global Optimization

@article{Hennig2012EntropySF,
  title={Entropy Search for Information-Efficient Global Optimization},
  author={Philipp Hennig and Christian J. Schuler},
  journal={Journal of Machine Learning Research},
  year={2012},
  volume={13},
  pages={1809-1837}
}
Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which… CONTINUE READING

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