报告地点：行健楼-526
邀请人：蔡邢菊副教授，姜波副教授
摘要：Optimization on Riemannian mainfolds, 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, e.g., blind source separation, computations on symmetric positive matrices, low-rank learning, graph similarity, community detection, and elastic shape analysis. In this short course, we first introduce Riemannian optimization though embedded submanifolds of linear spaces without requiring preliminaries on manifold geometry. The generic concepts for Riemannian optimization are then given and discussed. Finally, a few Riemannian optimization algorithms are discussed and a few concrete examples are used to show the performance of the algorithms.
报告人简介：黄文于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。
