A Search Peak inside a Fixed Opportunity-Cost List

Authors

  • Adrian Butler Author
  • Vincent Mason Author
  • Calvin Hughes Author

Abstract

Peak-directed search creates a list, while fixed-design opportunity-cost selection compares candidates within a list. These stages should be evaluated separately and then jointly. Independent verification can reveal optimism in the discovered extreme. Loss within the final list cannot diagnose a valuable region the search never found, so candidate coverage needs its own evidence. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.

References

  1. S. Gao, L. Shi, and Z. Zhang, “A Peak-Over-Threshold Search Method for Global Optimization,” Automatica, vol. 89, pp. 83–91, 2018.
  2. S. Gao and L. Shi, “An Optimal Opportunity Cost Selection Procedure for a Fixed Number of Designs,” Proceedings of the 2014 Winter Simulation Conference, pp. 3952–3958, 2014.
  3. Chick, Stephen E., and Koichiro Inoue. “New Two-Stage and Sequential Procedures for Selecting the Best Simulated System.” Operations Research, vol. 49, no. 5, 2001, pp. 732–743. https://doi.org/10.1287/opre.49.5.732.10615.
  4. Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
  5. Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
  6. Sacks, Jerome, William J. Welch, Toby J. Mitchell, and Henry P. Wynn. “Design and Analysis of Computer Experiments.” Statistical Science, vol. 4, no. 4, 1989, pp. 409–423. https://doi.org/10.1214/ss/1177012413.
  7. Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.
  8. Campi, Marco C., and Simone Garatti. “The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs.” SIAM Journal on Optimization, vol. 19, no. 3, 2008, pp. 1211–1230. https://doi.org/10.1137/07069821X.
  9. Glasserman, Paul. Monte Carlo Methods in Financial Engineering. Springer, 2004. https://doi.org/10.1007/978-0-387-21617-1.
  10. Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.

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Published

2026-10-08