学术报告
学术报告: Riemannian Optimization with its Application to BlindDeconvolution Problem
编辑:发布时间:2018年04月19日

报告人:黄文博士

美国莱斯大学

题目: Riemannian Optimization with its Application to BlindDeconvolution Problem

时间:2018年4月27日 11:00

地点:海韵园行政楼B313

摘要:Optimization on Riemannian manifolds, also called Riemannianoptimization, considers finding an optimum ofa real-valued function defined on a Riemannian manifold. Riemannianoptimization has been a topic ofmuch interest over the past few years due to many important applications, e.g., blind source separation,computations on symmetric positive matrices, low-rank learning, graphsimilarity, and elasticshape analysis. In this presentation, the framework of Riemannian optimization is introduced, and the history andcurrent state of Riemannian optimization algorithms are briefly reviewed. Optimizationproblems in the blinddeconvolution are used to demonstrate the efficiency and effectivenessof Riemannian optimization.

联系人:郭淑敏老师

 

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