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A DYNAMIC PROGRAMMING APPROACH TO DISTRIBUTION-CONSTRAINED OPTIMAL STOPPING
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2022 (English)In: The Annals of Applied Probability, ISSN 1050-5164, E-ISSN 2168-8737, Vol. 32, no 3, p. 1902-1928Article in journal (Refereed) Published
Abstract [en]

We consider an optimal stopping problem where a constraint is placed on the distribution of the stopping time. Reformulating the problem in terms of so-called measure-valued martingales enables us to transform the distributional constraint into an initial condition and view the problem as a stochastic control problem; we establish the corresponding dynamic programming principle. The method offers a systematic approach for solving the problem for general constraints and under weak assumptions on the cost function. In addition, we provide certain continuity results for the value of the problem viewed as a function of its distributional constraint.

Place, publisher, year, edition, pages
Institute of Mathematical Statistics , 2022. Vol. 32, no 3, p. 1902-1928
Keywords [en]
Distribution-constrained optimal stopping, measure-valued martingales, dynamic programming principle, optimal transport, measurable selection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-314186DOI: 10.1214/21-AAP1724ISI: 000803816900011Scopus ID: 2-s2.0-85132975991OAI: oai:DiVA.org:kth-314186DiVA, id: diva2:1671707
Note

QC 20220617

Available from: 2022-06-17 Created: 2022-06-17 Last updated: 2023-03-22Bibliographically approved

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Källblad Nordin, Sigrid

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