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math.OC2026
First-Order Methods for Solving Convex (Strongly) Concave Minimax Problems with Functional Constraints
Yangyang Xu, Xinhao Ying
Minimax problems arise in many applications, including robust learning and Stackelberg models. Most existing methods for minimax problems address unconstrained or projection-friend…
math.OC2026
Alternating Direction Method of Multipliers for nonlinear constrained convex problems and applications to distributed resource allocation and constrained machine learning
Zhengjie Xiong, Yangyang Xu
We study a class of structured convex optimization problems, which have a two-block separable objective and nonlinear functional constraints as well as affine constraints that coup…
math.OC2026
A variance reduced framework for (non)smooth nonconvex-nonconcave stochastic minimax problems with extended Kurdyka-Lojasiewicz property
Muhammad Khan, Yangyang Xu
In this paper, we study stochastic constrained minimax optimization problems with nonconvex-nonconcave structure, a central problem in modern machine learning, for which reliable a…