报告主题：Algorithmic Design for Big Data Related Optimization
报 告 人：陈彩华 副教授 （南京大学）
报告时间：2020年11月6日（周五） 15:00
报告地点：G507
邀 请 人：徐姿
主办部门：理学院数学系
报告摘要：We live in the age of big data. The 5 characteristics of big data - volume, value, variety, velocity and veracity - have a significant impact on optimization. In this talk, we discuss some thinking of algorithmic design for big data related optimization problems. Specifically, we consider splitting methods for large scale structure optimization, to analyze the data with high volume and low value density. We also design efficient algorithms for distribution robust optimization, to cope with brittle veracity in data analysis. Finally, we propose LP-based approach for Markov Decision Process, which lays a deep ground in sequential decision making with dynamic data generated at a high velocity.
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