报告题目：Online Linear Programming: Applications and Extensions
报 告 人：叶荫宇 斯坦福大学
报告时间：2023年6月20日(周二) 10:00-11:30
报告地点：中国科学院大学中关村校区教学楼S406
腾讯会议：903-292-238
内容摘要
A natural optimization model that formulates many online learning, resource allocations and dynamic decision-making with uncertainty is online linear programming (OLP) where the noisy constraint column vectors, along with the objective coefficients and decision variables, are revealed and decided sequentially. We review the near optimal algorithms and theories for solving this surprisingly general class of online problems under the assumption of random order of arrivals and/or stationary distributions of the input data. Then we present few recent developments of the model/algorithm, including a fast online algorithm as a pre-solver for solving large-scale offline (binary) LPs, an interior-point online algorithm to address “fairness” for resource allocation, a provable logarithmic regret bound for the Bandits with Knapsacks (BwK) problem, an extension to online Fisher markets with the Eisenberg/Gale social welfare of individuals.
主讲人简介
叶荫宇教授现任斯坦福大学管理科学与工程系及计算数学工程研究院的杰出终身教授。叶教授主要研究方向为优化、复杂性理论、算法设计与分析、数学规划应用、运筹学以及随机与鲁棒决策等。叶荫宇教授曾荣获多个重要研究奖项。特别的，2009年叶教授被授予约翰•冯•诺依曼理论奖，以表彰他对运筹学和管理科学理论的持续贡献。2012年，他成为国际数学规划大会（ISMP）Tseng Lectureship奖的首位获奖者；2014年，叶荫宇成为美国应用数学学会（SIAM）三年一度的优化大奖（Optimization Prize）的获奖者。根据谷歌学术统计，目前他的文章被引用总计超过53000次。
