Bayesian Optimization via Exact Penalty

Author:

Zhao Jiangyan1,Xu Jin12

Affiliation:

1. School of Statistics, East China Normal University, Shanghai, China

2. Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, East China Normal University, Shanghai, China

Funder

Key Laboratory of Advanced Theory and Application in Statistics and Data Science – Ministry of Education of China

Publisher

Informa UK Limited

Reference65 articles.

1. Alvi A. (2019) “Practical Bayesian Optimisation for Hyperparameter Tuning ” Ph. D. thesis University of Oxford.

2. “ADMMBO: Bayesian Optimization with Unknown Constraints Using ADMM,”;Ariafar S.;Journal of Machine Learning Research,2019

3. A surrogate-model-based method for constrained optimization

4. Balandat, M., Karrer, B., Jiang, D. R., Daulton, S., Letham, B., Wilson, A. G., and Bakshy, E. (2020), “BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization,” in Advances in Neural Information Processing Systems (Vol. 33), eds. H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, pp. 21524–21538. Curran Associates, Inc.

5. Barbosa, H. J., and Lemonge, A. C. (2002), “An Adaptive Penalty Scheme in Genetic Algorithms for Constrained Optimization Problems,” in Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, pp. 287–294.

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