报告题目: A series of talks on Riemannian Optimization

报告人：黄文教授（厦门大学）

时间：2025年9月15日16日17日,下午14:30--17:30

地点：数日韩无码 203报告厅

摘要: Optimization on Riemannian manifolds, also called Riemannian optimization, considers finding an optimum of a real-valued function 

defined on a Riemannian manifold. Riemannian optimization has been a topic of much interest over the past few years due to many important 

applications, including but not limited to blind source separation, computations on symmetric positive matrices, low-rank learning, graph 

similarity, community detection, and elastic shape analysis. This series of talks introduces the concepts in Riemannian optimization,

 highlights the difficulties and differences from traditional optimization algorithms, and presents our recent research results. 

Specifically, it consists of three parts:

(i) Preliminaries on Riemannian optimization, which introduces the basic concepts;

(ii) Smooth optimization on manifolds, in which we highlight some differences to Euclidean optimization; and

(iii) Nonsmooth optimization on manifolds, which includes some of our recent research results.

报告人简介：黄文，厦门大学教授。2014年在佛罗里达州立大学获得应用与计算数学博士学位。之后于2014年至2016年在比利时新鲁汶大学数学工程担任博

士后。2016年至2018年，他加入美国莱斯大学计算与应用数学系担任法伊佛博士后讲师，并于2018年9月加入厦门大学。他的主要研究兴趣在黎曼流形上的

优化算法及其应用，包括弹性形状分析，独立成分分析，相位复原问题，盲解卷积问题，对称正定阵上的计算，角色成分分析，等大规模问题的理论以及算

法实现。他开发了用来分析生物进化树的软件工具包TreeScaper以及用来解决流形优化问题的C++软件工具包ROPTLIB。