题    目：Derivative-free methods for nonlinear least squares problems

报告人：范金燕 教授

单    位：上海交通大学

时    间：3月28日 11:00

地    点：郑州校区九章学堂南楼C座302

摘   要：In this talk we consider nonlinear least squares problems for which the exact Jacobians are not available and replaced by probabilistic or random models. Problems of this nature arise in important practical applications, such as the data assimilation in weather prediction and the estimation of the merit function in deep learning. We will present some derivative-free algorithms for such problems and show the almost sure global convergence and complexity of the algorithms.

简   介：范金燕，上海交通大学数学科学学院教授。主要从事非线性最优化的理论和方法研究，在非线性方程组、完全正优化、张量计算等方面取得了一系列成果。现担任中国运筹学会数学规划分会副理事长，曾获中国青年女科学家奖、中国青年科技奖，入选国家“万人计划”科技创新领军人才。