Matroid Bayesian Online Selection
Ian DeHaan, Kanstantsin Pashkovich

TL;DR
This paper investigates Bayesian online selection problems with matroid constraints, providing a polynomial-time approximation scheme for laminar matroids under certain conditions, but proving hardness results for graphic matroids.
Contribution
It introduces a PTAS for laminar matroid constraints in Bayesian online selection and establishes PSPACE-hardness for approximation in graphic matroids.
Findings
PTAS exists for laminar matroids with left-to-right order
PSPACE-hardness of approximation for graphic matroids
No improved approximation possible for graphic matroids under current assumptions
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 [Kleinberg and Weinberg, 2012]. We turn our attention to the online optimum, or "philosopher", and ask how well…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Data Stream Mining Techniques · Distributed Sensor Networks and Detection Algorithms
