报告题目：Non-convergence Analysis of Randomized Direct Search

报告人：张在坤 教授（中山大学）

报告时间：2025年5月22日上午10:00-12:00

地点：日韩无码 425报告厅

报告摘要：Direct search is a popular method in derivative-free optimization. Randomized direct search 

has attracted increasing attention in recent years due to both its practical success and theoretical 

appeal. It is proved to converge under certain conditions at the same global rate as its deterministic 

counterpart, but the cost per iteration is much lower, leading to significant advantages in practice. 

However, a fundamental question has been lacking a systematic theoretical investigation: when will 

randomized direct search fail to converge? We answer this question by establishing the non-convergence

 theory of randomized direct search. We prove that randomized direct search fails to converge if the

 searching set is probabilistic ascent. Our theory does not only deepen our understanding of the 

behavior of the algorithm, but also clarifies the limit of reducing the cost per iteration by 

randomization, and hence provides guidance for practical implementations of randomized direct search.

This is a joint work with Cunxin Huang, a Ph.D. student funded by the Hong Kong Ph.D. Fellowship Scheme.

报告人简介：张在坤 2007 年本科毕业于吉林大学，2012 年博士毕业于中国科日韩无码 ，目前为中山大学日韩无码 教授、

博士生导师、逸仙优秀学者。他的主要研究兴趣为最优化理论与算法，特别是不依赖无导数优化、基于不精确信息的

优化、随机化方法等。他的代表作发表于 Mathematical Programming、 SIAM Journal on Optimization、SIAM 

Journal on Scientific Computing 等杂志。张在坤曾主持香港研究资助局 ECS/GRF 项目五项, 参与科技部国家重

点研发计划一项，并于 2023 年入选国家级青年人才计划。2024 年，张在坤的团队被授予中国运筹学会科学技术奖

“运筹应用奖”，以表彰其对无导数优化算法、软件及其工业应用作出的贡献。