报告题目 (Title)：Recent Advances in Quasi-Newton Methods （拟牛顿法的最新进展）
报告人 (Speaker)：罗络 副研究员（复旦大学大数据学院）
报告时间 (Time)：2023年11月7日 (周二) 16:20
报告地点 (Place)：校本部GJ303
邀请人(Inviter)：徐姿 教授
主办部门：永利数学系
报告摘要：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.
