报告题目: Riemannian Optimization and its Application to Phase Retrieval Problem

报告人: Dr. Huang Wen, Universite catholique de Louvain, Belgium 

Abstract: Optimization on Riemannian manifolds, also called Riemannian optimization, considers finding an optimum of a real-valued function f defined on a Riemannian manifold M. Riemannian optimization has been a topic of much interest over the past few year 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 presentation, the framework of Riemannian optimization is introduced, and the history and current state of Riemannian optimization algorithms are briefly reviewed. Optimization problems in the Phase Lift framework is used to demonstrate the efficiency and effectiveness of Riemannian optimization.

时间地点: 4月7日 16:00-17:00，环境楼316