报告题目：Randomized Local Search for a Family of Experimental Design Problems

报告摘要：We consider a general p-norm objective for experimental design problems that captures some well-studied objectives (D/A/E-design) as special cases. We prove that a randomized local search approach provides a unified algorithm to solve this problem for all nonnegative integer p. This provides the first approximation algorithm for the general p-norm objective, and a nice interpolation of the best known bounds of the special cases.

报告人简介：周宏，福州大学数学与统计学院副教授；2020年博士毕业于滑铁卢大学；2021年在滑铁卢大学从事博士后研究工作；2022年3月加入福州大学离散数学与理论计算机研究中心。主要研究方向为组合优化、近似算法、图谱理论等。

报告时间：2023年10月14日10:00-11:00

报告地点：武之楼 308