Warm starting Bayesian optimization

@article{Poloczek2016WarmSB,
  title={Warm starting Bayesian optimization},
  author={Matthias Poloczek and Jialei Wang and Peter I. Frazier},
  journal={2016 Winter Simulation Conference (WSC)},
  year={2016},
  pages={770-781}
}
  • Matthias Poloczek, Jialei Wang, Peter I. Frazier
  • Published 2016
  • Mathematics, Computer Science
  • 2016 Winter Simulation Conference (WSC)
  • We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems. This is useful when optimizing the output of a stochastic simulator that fails to provide derivative information, for which Bayesian optimization methods are well-suited. Solving sequences of related optimization problems arises when making several business decisions using one optimization model and input data… CONTINUE READING

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