报告人：吴中明 南京信息工程大学副教授

报告时间：5月13日9:00

报告地点：腾 讯 ID：938 777 476

主办单位：数学与统计学院

欢迎光临！

  In this talk, we introduce several splitting methods for a class of structured convex/nonconvex optimization problems, which capture many applications in signal and image processing, statistics and machine learning. We first propose an inertial proximal gradient method for minimizing the sum of two possibly nonconvex functions. This method includes two different inertial steps and adopts the Bregman regularization in solving the subproblem. To overcome the parameter constraints, we further propose a nonmonotone line search strategy to make the parameter selections more flexible. Moreover, we introduce the inexact primal-dual splitting methods to solve a class of more general convex composited optimization problems. Some numerical results on sparse optimization and image processing problems are reported to demonstrate the effectiveness and superiority of the proposed methods.

  吴中明，南京信息工程大学副教授，硕士生导师，2019年12月博士毕业于东南大学，曾前往新加坡国立大学学习交流一年。研究方向为最优化理论、算法及其应用。主持国家自然科学基金青年科学基金和江苏高校哲学社科基金项目，入选2020年江苏省“双创博士”人才计划。在 Computational Optimization and Applications、Journal of Optimization Theory and Applications、Journal of Global Optimization、Mathematics of Computation、International Transactions in Operational Research、系统工程理论与实践等期刊发表论文二十余篇。