Asymptotic optimality of myopic ranking and selection procedures

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Article number110896
Journal / PublicationAutomatica
Volume151
Online published13 Feb 2023
Publication statusPublished - May 2023

Abstract

Ranking and selection (R&S) is a popular model for studying discrete-event dynamic systems. It aims to select the best design (the design with the largest mean performance) from a finite set, where the mean of each design is unknown and has to be learned by samples. Great research efforts have been devoted to this problem in the literature for developing procedures with superior empirical performance and showing their optimality. In these efforts, myopic procedures were popular. They select the best design using a “naive” mechanism of iteratively and myopically improving an approximation of the objective measure. Although they are based on simple heuristics and lack theoretical support, they turned out highly effective, and often achieved competitive empirical performance compared to procedures that were proposed later and shown to be asymptotically optimal. In this paper, we theoretically analyze these myopic procedures and prove that they also satisfy the optimality conditions of R&S, just like some other popular R&S methods. It explains the good performance of myopic procedures in various numerical tests, and provides good insight into the structure and theoretical development of efficient R&S procedures. © 2023 Elsevier Ltd

Research Area(s)

  • Asymptotic optimality, Discrete-event dynamic systems, Myopic procedure, Optimal computing budget allocation, Ranking and selection

Citation Format(s)

Asymptotic optimality of myopic ranking and selection procedures. / Li, Yanwen; Gao, Siyang; Shi, Tony Z.
In: Automatica, Vol. 151, 110896, 05.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review