报告题目 (Title)：Adaptive Dimension Reduction for Overlapping Group Sparsity（重叠组稀疏性的自适应维数约简）
报告人 (Speaker)：梁经纬（上海交通大学）
报告时间 (Time)：2025年12月11日(周四) 9：30
报告地点 (Place)：校本部GJ303
邀请人(Inviter)：周安娃
报告摘要：Typical dimension reduction techniques for sparse optimization involve screening strategies based on a dual certificate derived from the first-order optimality condition, approximating the gradients or exploiting some inherent low dimensional structure that an optimization algorithm promotes. Screening rules for overlapping group lasso are generally less developed because the subgradient structure is more complex and the link between sparsity pattern and the dual vector is generally indirect. In this talk, I will present a new strategy for certifying the support of the overlapping group lasso and demonstrate how this can be applied significantly accelerate the performance of numerical methods.