Online Linear Programming with Batching

Abstract:We study Online Linear Programming (OLP) with batching. The planning horizon is cut into $K$ batches, and decisions on orders can be delayed to the end of their associated batch. The ability to delay decisions improves operational performance, as measured by regret. We study two questions: (1) What is a lower bound on the regret as a function of $K$ and the length of the planning horizon? (2) Which algorithms can achieve this regret lower bound? This paper analyzes these questions when the distribution of the reward has a continuous support. We provide an $\Omega(\log K)$ regret lower bound in the single-resource case, and we provide pricing algorithms having an $O(\log K)$ regret in the setting with Poisson arrivals and multiple types of resources. All the algorithms update the prices at most $K$ times and only delay orders of the first and the last batches. All the regret bounds are independent of the length of the planning horizon. Finally, we study a more realistic large support setting where the number of distinct order types is finite but scales with the total number of orders. We prove that our $\Theta(\log K)$ bounds still hold for the batching operation of a multisecretary problem with discretized uniform rewards in this large support setting, provided the support grows fast enough. This suggests that the continuous support setting that is the paper's focus serves as a useful theoretical surrogate for the more realistic large finite support setting.

Machine Learning (cs.LG); Optimization and Control (math.OC)

arXiv:2408.00310 [cs.LG]

(or arXiv:2408.00310v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2408.00310 Focus to learn more arXiv-issued DOI via DataCite

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