Bayesian Optimization Allowing for Common Random Numbers

References

• Ankenman B, Nelson BL, Staum J (2010) Stochastic kriging for simulation metamodeling. Oper. Res. 58(2):371–382.Link, Google Scholar
• Chen X, Ankenman BE, Nelson BL (2012) The effects of common random numbers on stochastic kriging metamodels. ACM Trans. Model. Comput. Simulation 22(2):1–20.Crossref, Google Scholar
• Chick SE, Inoue K (2001) New two-stage and sequential procedures for selecting the best simulated system. Oper. Res. 49(5):732–743.Link, Google Scholar
• Frazier P (2012) Tutorial: Optimization via simulation with Bayesian statistics and dynamic programming. Proc. 2012 Winter Simulation Conf. (IEEE), 1–16.Google Scholar
• Frazier P, Powell W, Dayanik S (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.Link, Google Scholar
• Fu MC, Hu JQ, Chen CH, Xiong X (2004) Optimal computing budget allocation under correlated sampling. Ingalls RG, Rossetti MD, Smith JS, Peters BA, eds. Proc. 2004 Winter Simulation Conf., vol. 1 (Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ), 608–616.Google Scholar
• Görder B, Kolonko M (2019) Ranking and selection: A new sequential Bayesian procedure for use with common random numbers. ACM Trans. Model. Comput. Simulation 29(1):1–24.Crossref, Google Scholar
• Huang D, Allen TT, Notz WI, Miller RA (2006a) Sequential kriging optimization using multiple-fidelity evaluations. Structural Multidisciplinary Optim. 32(5):369–382.Crossref, Google Scholar
• Huang D, Allen TT, Notz WI, Zeng N (2006b) Global optimization of stochastic black-box systems via sequential kriging meta-models. J. Global Optim. 34(3):441–466.Crossref, Google Scholar
• Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.Crossref, Google Scholar
• Kandasamy K, Krishnamurthy A, Schneider J, Póczos B (2018) Parallelised Bayesian optimisation via Thompson sampling. Internat. Conf. Artificial Intelligence Statist. Proc. Machine Learn. Res. (PMLR), 133–142.Google Scholar
• Kim SH (2013) Statistical ranking and selection. Gass SI, Fu MC, eds. Encyclopedia of Operations Research and Management Science (Springer, Boston), 1459–1469.Google Scholar
• Nelson BL, Matejcik FJ (1995) Using common random numbers for indifference-zone selection and multiple comparisons in simulation. Management Sci. 41(12):1935–1945.Link, Google Scholar
• Pasupathy R, Henderson SG (2006) A testbed of simulation-optimization problems. Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM, eds. Proc. 2006 Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ), 255–263.Google Scholar
• Pearce M, Branke J (2017) Bayesian simulation optimization with input uncertainty. Chan WKV, D’Ambrogio A, Zacharewicz G, Mustafee N, Wainer G, Page E, eds. Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ), 2268–2278.Google Scholar
• Picheny V (2015) Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction. Statist. Comput. 25(6):1265–1280.Crossref, Google Scholar
• Poloczek M, Wang J, Frazier P (2017) Multi-information source optimization. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., Red Hook, NY), 4288–4298.Google Scholar
• Rasmussen CE (2003) Gaussian processes in machine learning. Summer School on Machine Learning. Lecture Notes in Computer Science, vol. 3176 (Springer, Berlin), 63–71.Google Scholar
• Salemi PL, Song E, Nelson BL, Staum J (2019) Gaussian Markov random fields for discrete optimization via simulation: Framework and algorithms. Oper. Res. 67(1):250–266.Link, Google Scholar
• Scott W, Frazier P, Powell W (2011) The correlated knowledge gradient for simulation optimization of continuous parameters using Gaussian process regression. SIAM J. Optim. 21(3):996–1026.Crossref, Google Scholar
• Srinivas N, Krause A, Kakade SM, Seeger M (2009) Gaussian process optimization in the bandit setting: No regret and experimental design. Preprint, submitted December 21, https://arxiv.org/abs/0912.3995.Google Scholar
• Swersky K, Snoek J, Adams RP (2013) Multi-task Bayesian optimization. CJC Burges, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 26 (Curran Associates, Inc, Red Hook, NY), 2004–2012.Google Scholar
• Toscano-Palmerin S, Frazier PI (2018) Bayesian optimization with expensive integrands. Preprint, submitted March 23, https://arxiv.org/abs/1803.08661.Google Scholar
• Villemonteix J, Vazquez E, Walter E (2009) An informational approach to the global optimization of expensive-to-evaluate functions. J. Global Optim. 44(4):509.Crossref, Google Scholar
• Wu J, Frazier P (2016) The parallel knowledge gradient method for batch Bayesian optimization. CJC Burges, Bottou L, Welling M, Ghahramani Z, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 29 (Curran Associates, Inc, Red Hook, NY), 3126–3134.Google Scholar
• Wu J, Poloczek M, Wilson AG, Frazier P (2017) Bayesian optimization with gradients. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., Red Hook, NY), 5267–5278.Google Scholar
• Xie J, Frazier PI, Chick SE (2016) Bayesian optimization via simulation with pairwise sampling and correlated prior beliefs. Oper. Res. 64(2):542–559.Link, Google Scholar
• Xu J, Nelson BL, Hong J (2010) Industrial strength compass: A comprehensive algorithm and software for optimization via simulation. ACM Trans. Model. Comput. Simulation 20(1):1–29.Crossref, Google Scholar