报告人：吕锡亮教授

报告题目：Stochastic regularization method for linear ill-posed problems

报告摘要：

(1) Due to rapid growth of data sizes in practical applications, in recent years stochastic optimization methods have received tremendous attention and proved to be efficient in various applications of science and technology including in particular the machine learning applications.

(2) In this talk we propose randomized Kaczmarz method, stochastic gradient descent method and stochastic mirror descent method for solving linear ill-posed inverse problems.  The convergence and convergence rate are provided. Several numerical examples validate the efficiency of the proposed algorithms.

报告时间：2022年11月26 日 下午15:00-18:00

报告形式：腾讯会议；会议号：712-632-121

获取会议密码请发邮件至：yangchang@hit.edu.cn

报告人简介：吕锡亮博士，武汉大学数学与统计学院教授。本科毕业于北京大学，并于新加坡国立大学获得硕士、博士学位，曾在马里兰大学、奥地利科学院RICAM研究所从事博士后研究，2010年加入武汉大学数学与统计学院。主要研究方向为偏微分方程数值解、偏微分方程最优控制、反问题理论和计算、机器学习等。