学术报告

A Semismooth Newton-Type Method for the Nearest

Doubly Stochastic Matrix Problem

李欣欣（吉林大学）

报告时间：2023年6月30日 星期五  17：00-18：00

报告地点：沙河主楼E404

腾讯会议：802-256-565

报告摘要: In this talk, we study a semismooth Newton-type method for the nearest doubly stochastic matrix problem where the nonsingularity of the Jacobian can fail. The optimality conditions for this problem are formulated as a system of strongly semismooth functions. We show that the nonsingularity of the Jacobian does not hold for this system. By exploiting the problem structure, we construct a modified two step semismooth Newton method that guarantees a nonsingular Jacobian matrix at each iteration, and that converges to the nearest doubly stochastic matrix quadratically.

报告人简介：李欣欣，吉林大学皇冠6686官网副教授，2010年在吉林大学皇冠6686官网获理学学士学位，2014年在香港浸会大学获博士学位。2019年受国家留学基金委资助，赴加拿大滑铁卢大学访学一年。研究领域包括一阶优化算法的理论及其在机器学习、图像处理中的应用。主持了国家自然科学基金青年项目一项，吉林省自然科学基金青年项目一项，在包括Mathematics of Operations Research, SIAM Journal on Imaging Sciences, Informs Journal on Computing, Journal of Scientific Computing, Computational Optimization and Applications, Journal of Global Optimization等期刊上发表了多篇论文。

邀请人：崔春风