Riemannian Optimization with its Application to Blind Deconvolution Problem

Date: 
Thursday, 19 April, 2018 - 15:00 - 16:15
Venue: 
LSB 219
Seminar Type: 
Seminar
Speaker Name: 
Dr. Wen HUANG
Affiliation: 
Rice University
Abstract: 

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, e.g., blind source separation, computations on symmetric positive matrices, low-rank learning, graph similarity, 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 blind deconvolution is used to demonstrate the efficiency and effectiveness of Riemannian optimization.