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Xin Chen:Efficient Algorithms for Minimizing Compositions of Convex Functions and Random Functions and Its Applications in Network Revenue Management
发布日期:2022-06-13  来源:   查看次数:

报告时间:2022年6月17日(星期五)上午9:00-10:30

报告地点:腾讯会议号:733-868-197

人:Xin Chen

工作单位:University of Illinois at Urbana-Champaign

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报告简介:

Abstract:Motivated by network revenue management problems using booking limit control and inventory models with random capacities, we study a class of nonconvex stochastic optimization in which the objective function is a composition of a convex function and a random function. Leveraging a convex reformulation via a variable transformation, we develop stochastic gradient-based algorithms and establish their sample and gradient complexities for achieving an epsilon-global optimal solution. Interestingly, our proposed Mirror Stochastic Gradient (MSG) method operating in the original variables achieves complexities that match the lower bound for solving stochastic convex optimization problems. Extensive numerical experiments on air-cargo network revenue management problems with random two-dimensional capacity, random consumption, and routing flexibility demonstrate the superior performance of our proposed MSG algorithm and booking limit control policies vs. state-of-the-art bid-price-based control policies.

报告人简介:Xin Chen is a professor at the University of Illinois at Urbana-Champaign. He obtained his PhD from MIT in 2003, MS from Chinese Academy of Sciences in 1998 and BS from Xiangtan University in 1995. His research interest lies in optimization, data analytics, revenue management and supply chain management. He received the Informs revenue management and pricing section prize in 2009. He is the coauthor of the book “The Logic of Logistics: Theory, Algorithms, and Applications for Logistics and Supply Chain Management (Second Edition, 2005, & Third Edition, 2014)”, and serving as the department editor of logistics and supply chain management of Naval Research Logistics and an associate editor of several journals including Operations Research, Management Science, and Production and Operations Management.


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