报告题目：Dynamic Learning Algorithms for Online Linear/Nonlinear Optimization Problem

演讲摘要：In this talk, we will study a class of online optimization problems. In this problem, the underlying optimization problem is a linear program, however, its constraint matrix is revealed column by column along with the corresponding objective coefficient and a decision variable has to be set each time a column is revealed without observing the future inputs. The goal is to maximize the overall objective function. In this talk, we provide a near-optimal algorithm for this general class of online problems under the assumption of random order of arrival of the columns and some mild conditions on the size of the LP right-hand-side input. Specifically, our learning-based algorithm works by dynamically updating a threshold price vector at geometric time intervals, where the dual prices learned from the revealed columns in the previous period are used to determine the sequential decisions in the current period. Due to the feature of dynamic learning, the competitiveness of our algorithm improves over the past study of the same problem. We also present a worst-case example showing that the performance of our algorithm is near-optimal. We will further extend the results to nonlinear optimization problems and discuss some extensions to high-dimensional decision-making cases

讲者简介：2007年本科毕业于清华大学数学系，2012年博士毕业于斯坦福大学管理科学与工程系。曾任职美国明尼苏达大学工业与系统工程系助理教授、（终身）副教授。 他主要研究方向为运筹优化、在线算法设计和运营管理。他在运筹优化和管理科学国际顶尖杂志如Operations Research等上发表40余篇论文，在国际国内会议上多次应邀做报告，并担任多个国际管理科学期刊副主编。曾获得多项运筹优化领域学术奖项，并担任多次国际学术会议组织者。 王子卓博士在工业界也有着丰富的项目经验，在美国时曾长期参与IBM定价项目，为企业大幅提升收益，也曾为希捷、美国运通等做过项目咨询，也曾在华尔街量化基金担任过研究员。2016年起，王子卓与他人共同创立杉数科技并担任CTO。杉数科技为国内大型物流、零售、制造型企业提供数据决策系统与服务，帮助企业显著提升运营效率。