Matroid Bayesian Online Selection
<|reference_start|>Matroid Bayesian Online Selection: 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 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 $\texttt{PSPACE}$-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.<|reference_end|>
@article{dehaan2024matroid,
    title={Matroid Bayesian Online Selection},
    author={Ian DeHaan and Kanstantsin Pashkovich},
    journal={arXiv preprint arXiv:2406.00224},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00224},
    primaryClass={cs.GT}
}