汇报标题 (Title)：Recent Advances in Quasi-Newton Methods （拟牛顿法的最新进展）
汇报人 (Speaker)：罗络 副钻研员（复旦大学大数据学院）
汇报功夫 (Time)：2023年11月7日 (周二) 16:20
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：We introduce symmetric rank-$k$ methods for convex optimization to demonstrate that block quasi-Newton methods have provably faster convergence rates compared to ordinary quasi-Newton methods. We also present block Broyden's methods and square quasi-Newton methods for solving general nonlinear equations with improved convergence. For specific minimax problems, we design partial quasi-Newton methods that leverage the unbalanced dimensionality, which results in complexity matching the cost for convex minimizing problems.
