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math.OC2022★ 1 cited
First-Order Methods for Nonsmooth Nonconvex Functional Constrained Optimization with or without Slater Points
Zhichao Jia, Benjamin Grimmer
Constrained optimization problems where both the objective and constraints may be nonsmooth and nonconvex arise across many learning and data science settings. In this paper, we sh…
math.OC2022
On Optimal Universal First-Order Methods for Minimizing Heterogeneous Sums
Benjamin Grimmer
This work considers minimizing a sum of convex functions, each with potentially different structure ranging from nonsmooth to smooth, Lipschitz to non-Lipschitz. Nesterov's univers…
math.OC2022
On the Linear Convergence of Extra-Gradient Methods for Nonconvex-Nonconcave Minimax Problems
Saeed Hajizadeh, Haihao Lu, Benjamin Grimmer
Recently, minimax optimization received renewed focus due to modern applications in machine learning, robust optimization, and reinforcement learning. The scale of these applicatio…