主讲人

（单位）

Sentao Miao

 (University of Colorado Boulder)

主持人

（单位）

李四杰、金子亮

（老司机导航 ）

讲座时间

2026年6月24日10点00分

讲座地点

人文社科综合楼310

主讲人简介

Sentao Miao is an Assistant Professor of Operations Management in Leeds School of Business at University of Colorado Boulder. Previously, he was an Assistant Professor in Bensadoun School of Retail Management & Desautels Faculty of Management at McGill University. His research interests are mainly in developing efficient learning and optimization algorithms with various applications in Operations Management. For methodologies, Sentao Miao focuses on statistical and machine learning algorithms such as online learning, multi-arm bandit problem, reinforcement learning; he is also interested in approximation algorithms with provable performance. For applications, he mainly works on operations management problems such as dynamic pricing, assortment selection, inventory control, etc. Sentao Miao obtained his PhD degree in Department of Industrial and Operations Engineering at University of Michigan.

讲座内容摘要

This paper proposes a practically efficient algorithm with optimal theoretical regret which solves the classical network revenue management (NRM) problem with unknown, nonparametric demand. Over a time horizon of length T, in each time period the retailer needs to decide prices of N types of products which are produced based on M types of resources with unreplenishable initial inventory. When demand is nonparametric with some mild assumptions, Miao and Wang (2021) is the first paper which proposes an algorithm with O(\text{poly}(N,M,\ln(T))\sqrt{T}) type of regret (in particular, \tilde{O}(N^{3.5}\sqrt{T}) plus additional high-order terms that are o(\sqrt{T}) with sufficiently large T\gg N). In this paper, we improve the previous result by proposing a primal-dual optimization algorithm which is not only more practical, but also with an improved regret of \tilde{O}(N^{3.25}\sqrt{T}) free from additional high-order terms. A key technical contribution of the proposed algorithm is the so-called demand balancing, which pairs the primal solution (i.e., the price) in each time period with another price to offset the violation of complementary slackness on resource inventory constraints. Numerical experiments compared with several benchmark algorithms further illustrate the effectiveness of our algorithm.