汇报标题 (Title)：Derivative-free algorithms for nonlinear least squares problems (非线性最幼二乘问题的无导数步骤)
汇报人 (Speaker)：范金燕 教授（上海交通大学数学科学学院）
汇报功夫 (Time)：2023年11月7日 (周二) 09:00
汇报地址 (Place)：校本部GJ303
约请人(Inviter)：徐姿 教授
主办部门：理学院数学系
汇报提要：In this talk we are concerned with nonlinear least squares problems for which the exact Jacobian is not available and replaced by a probabilistic or random model. 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.
