2012/08/13 by Patrick Jaillet, Xin Lu, Jaillet, Patrick +1 · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Age of Information Optimization #Computer Science and Game Theory (cs.GT) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Optimization and Search Problems #cs.DS #cs.GT
paper · pdf · doi:10.48550/arxiv.1208.2596
arxiv created 2012/08/13 · openalex publication_date 2012/08/13 · arxiv updated 2015/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we study a general online linear programming problem whose formulation encompasses many practical dynamic resource allocation problems, including internet advertising display applications, revenue management, various routing, packing, and auction problems. We propose a model, which under mild assumptions, allows us to design near-optimal learning-based online algorithms that do not require the a priori knowledge about the total number of online requests to come, a first of its kind. We then consider two variants of the problem that relax the initial assumptions imposed on the proposed model.