时间&地点：
2026.1.7, 9:00—11:30 (行健楼526);
2026.1.7, 15:10—17:40 (学正楼203);
2026.1.8, 9:30—12:00 (行健楼526);
邀请人：姜波教授
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;
(iii) Nonsmooth optimization on manifolds, which includes some of our recent research results.
黄文教授，2014年毕业于佛罗里达州立大学，获得应用与计算数学博士学位。2014年至2016年在比利时新鲁汶大学数学工程系担任博士后。2016年至2018年在美国莱斯大学计算与应用数学系担任法伊佛博士后讲师。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。研究成果发表在SIOPT，SISC，MATH PROGRAM，NUMER MATH等主流期刊。主持国家自然科学基金青年及面上项目。现任中国运筹学会理事，中国运筹学会数学规划分会青年理事，中国运筹学会算法软件与应用分会常务理事，中国数学会计算数学分会理事，并于2021年入选国家级高层次人才青年项目。
