Technical note—Knowledge gradient for selection with covariates : Consistency and computation

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

9 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)496-507
Number of pages12
Journal / PublicationNaval Research Logistics
Volume69
Issue number3
Online published7 Oct 2021
Publication statusPublished - Apr 2022
Externally publishedYes

Abstract

Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to solve optimization problems. In this paper, we consider the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the covariates. In this context, we prove that under minimal assumptions, the sampling policy based on knowledge gradient is consistent, in the sense that following the policy the best alternative as a function of the covariates will be identified almost surely as the number of samples grows. We also propose a stochastic gradient ascent algorithm for computing the sampling policy and demonstrate its performance via numerical experiments.

Research Area(s)

  • consistency, covariates, knowledge gradient, selection of the best

Bibliographic Note

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