Mingyu Xiao: Solving hard problems with theoretical guarantee

Abstract

Combinatorial optimization plays an important role in AI and real life. However, many optimization problems are NP hard, that is to say, there is no polynomial-time algorithm for them under reasonable assumptions. In practice, we have designed fast heuristic algorithms and exact algorithms for many of these problems, and they have a very good performance on tested benchmark instances. On the other hand, theoretical algorithms, may not be so practical, solve the problems with theoretical guarantees of running-time bound and solution quality, etc. In this talk, I will discuss the differences between theoretical and practical algorithms, and take the maximum independent set problem as an example to introduce exact algorithms with theoretical running-time bounds.

Speaker Bio

2008年在香港中文大学获得计算机博士学位之后进入电子科技大学工作，现在为电子科技大学计算机学院教授，副院长。主要从事算法分析与设计、机制设计与博弈论、人工智能中的基础算法理论等方向的研究，在Information and Computation、JCSS、Algorithmica、ACM/IEEE Trans.、ICALP、IJCAI、AAAI、WWW、INCOFOM等算法、人工智能领域顶级期刊和会议上发表论文超过100篇，撰写英文专著1部，主持（完成）国家自然科学基金项目5项。是参数算法和精确算法国内外知名的学者。