2025/01/29 by Jianyu Xu, Xuan Wang, Xu, Jianyu +6 · 1 citation
Decision Sciences · #Auction Theory and Applications #Game Theory and Applications #cs.LG #math.OC #stat.ML
paper · pdf · doi:10.48550/arxiv.2501.18049
openalex publication_date 2025/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
We study online learning for a seller that jointly chooses per-period inventory positions and a uniform price, then fulfills realized demand through a downstream allocation. The main difficulty is not only demand learning: the price shifts demand and reshapes the transportation LP, making the population objective globally non-convex and non-smooth. To solve this problem, we propose OCSAA, an algorithm that exploits demand observations through counterfactual translation and proposes joint (price, inventory) decisions through lower-confidence optimism. OCSAA admits a polynomial-time additive-accuracy implementation for rational-polytope inventory sets. We prove a high-probability \widetilde O(√ T) regret guarantee and establish a matching-in-T information-theoretic lower bound. Our results illustrate an effective integration of statistical learning methodologies with complex operations research problems.