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Article: Technical Note—knowledge Gradient For Selection With Covariates: Consistency And Computation

TitleTechnical Note—knowledge Gradient For Selection With Covariates: Consistency And Computation
Authors
Issue Date2021
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://as.wiley.com/WileyCDA/WileyTitle/productCd-NAV.html
Citation
Naval Research Logistics, 2021, v. 69 n. 3, p. 496-507 How to Cite?
AbstractKnowledge 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.
DescriptionNational Natural Science Foundation of China, 72001140; 72091211; 71991473; “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, 19CG14; Hong Kong Research Grants Council, GRF 17201520; 16211417
Persistent Identifierhttp://hdl.handle.net/10722/305707
ISSN
2023 Impact Factor: 1.9
2023 SCImago Journal Rankings: 1.260
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDing, L-
dc.contributor.authorHong, LJ-
dc.contributor.authorShen, H-
dc.contributor.authorZhang, X-
dc.date.accessioned2021-10-20T10:13:11Z-
dc.date.available2021-10-20T10:13:11Z-
dc.date.issued2021-
dc.identifier.citationNaval Research Logistics, 2021, v. 69 n. 3, p. 496-507-
dc.identifier.issn0894-069X-
dc.identifier.urihttp://hdl.handle.net/10722/305707-
dc.descriptionNational Natural Science Foundation of China, 72001140; 72091211; 71991473; “Chenguang Program” supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, 19CG14; Hong Kong Research Grants Council, GRF 17201520; 16211417-
dc.description.abstractKnowledge 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.-
dc.languageeng-
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://as.wiley.com/WileyCDA/WileyTitle/productCd-NAV.html-
dc.relation.ispartofNaval Research Logistics-
dc.rightsSubmitted (preprint) Version This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Accepted (peer-reviewed) Version This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.titleTechnical Note—knowledge Gradient For Selection With Covariates: Consistency And Computation-
dc.typeArticle-
dc.identifier.emailZhang, X: xiaoweiz@hku.hk-
dc.identifier.authorityZhang, X=rp02554-
dc.description.naturepostprint-
dc.identifier.doi10.1002/nav.22028-
dc.identifier.scopuseid_2-s2.0-85116470678-
dc.identifier.hkuros327190-
dc.identifier.volume69-
dc.identifier.issue3-
dc.identifier.spage496-
dc.identifier.epage507-
dc.identifier.isiWOS:000704307900001-
dc.publisher.placeUnited States-

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