Feb. 13, 2024, 5:42 a.m. | Wenzhi Gao Chunlin Sun Chenyu Xue Dongdong Ge Yinyu Ye

cs.LG updates on arXiv.org arxiv.org

Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient first-order online learning algorithms. Despite the empirical success of first-order methods, they typically achieve a regret no better than $\mathcal{O}(\sqrt{T})$, which is suboptimal compared to the $\mathcal{O}(\log T)$ bound guaranteed by the state-of-the-art linear programming (LP)-based online algorithms. This paper establishes several important facts about online linear programming, which unveils the challenge for first-order-method-based online algorithms to achieve …

algorithms breaking cs.lg decision linear making management math.oc online learning programming research revenue role success

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Architect

@ Dyson | India - Bengaluru IT Capability Centre

GTM Operation and Marketing Data Analyst

@ DataVisor | Toronto, Ontario, Canada - Remote

Associate - Strategy & Business Intelligence

@ Hitachi | (HE)Office Rotterdam

Senior Executive - Data Analysis

@ Publicis Groupe | Beirut, Lebanon