欢迎进入深圳大学数学与统计学院!

您当前所在位置: 首页>科学研究>学术交流

科学研究

快速导航

重要通知

学术报告一百零三:Low Tucker rank tensor recovery via iteratively reweighted algorithms

来源:本站 作者:cs 时间:2018-12-12 15次

数学与统计学院学术报告[2018] 103号

(高水平大学建设系列报告222号)

报告题目:Low Tucker rank tensor recovery via iteratively reweightedalgorithms

报告人:李昱帆  副研究员  (中山大学珠海校区)

报告时间:2018年12月13日14:00-15:00

报告地点:科技楼501

报告内容:

In

this talk, we consider a non-convex Lp norm relaxation model for low Tucker rank

tensor recovery problem, and equivalently transform it to a non-convex

minimization problem with separable structure by introducing series of auxiliary

variables. In particular, we propose two alternating direction method of

multipliers (ADMM) based on exact and inexact iteratively reweighted algorithms

to solve the obtained non-convex relaxation problem respectively, which are

proved to be convergent. We implement the proposed algorithms in numerical

experiments for solving low Tucker rank tensor recovery problem on simulation

data and real data, and compare them with other existing state-of-art

algorithms. Numerical results show the effectiveness of the proposed algorithms

for solving low rank tensor recovery problem and image recovery.


报告人简历:

李昱帆,中山大学珠海校区副研究员,主要研究方向为最优化理论与算法,具体为稀疏优化及张量优化.


欢迎感兴趣的师生参加!

数学与统计学院

2018年12月11日

深圳大学数学与统计学院