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Matroid Bayesian Online Selection

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Algorithmic Game Theory (SAGT 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15156))

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Abstract

We study a class of Bayesian online selection problems with matroid constraints. Consider a vendor who has several items to sell, with the set of sold items being subject to some structural constraints, e.g., the set of sold items should be independent with respect to some matroid. Each item has an offer value drawn independently from a known distribution. Given distribution information for each item, the vendor wishes to maximize their expected revenue by carefully choosing which offers to accept as they arrive.

Such problems have been studied extensively when the vendor’s revenue is compared with the offline optimum, referred to as the “prophet”. In this setting, a tight 2-competitive algorithm is known when the vendor is limited to selling independent sets from a matroid [29]. We turn our attention to the online optimum, or “philosopher”, and ask how well the vendor can do with polynomial-time computation, compared to a vendor with unlimited computation but with the same limited distribution information about offers.

We show that when the underlying constraints are laminar and the arrival of buyers follows a natural “left-to-right” order, there is a Polynomial-Time Approximation Scheme for maximizing the vendor’s revenue. We also show that such a result is impossible for the related case when the underlying constraints correspond to a graphic matroid. In particular, it is PSPACE-hard to approximate the philosopher’s expected revenue to some fixed constant \(\alpha < 1\); moreover, this cannot be alleviated by requirements on the arrival order in the case of graphic matroids.

I. DeHaan—Supported by an NSERC Canada Graduate Scholarship.

K. Pashkovich—Supported by NSERC Discovery Grants Program RGPIN-2020-04346.

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Authors and Affiliations

  1. Department of Combinatorics and Optimization, University of Waterloo, Waterloo, Canada

    Ian DeHaan & Kanstantsin Pashkovich

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  1. Ian DeHaan
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Correspondence to Kanstantsin Pashkovich .

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  1. Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands

    Guido Schäfer

  2. King's College London, London, UK

    Carmine Ventre

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DeHaan, I., Pashkovich, K. (2024). Matroid Bayesian Online Selection. In: Schäfer, G., Ventre, C. (eds) Algorithmic Game Theory. SAGT 2024. Lecture Notes in Computer Science, vol 15156. Springer, Cham. https://doi.org/10.1007/978-3-031-71033-9_23

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  • DOI: https://doi.org/10.1007/978-3-031-71033-9_23

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