This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the …
A Böhm - arXiv preprint arXiv:2201.12247, 2022 - arxiv.org
We investigate a structured class of nonconvex-nonconcave min-max problems exhibiting so-called\emph {weak Minty} solutions, a notion which was only recently introduced, but is …
T Zheng, L Zhu, AMC So… - Advances in Neural …, 2023 - proceedings.neurips.cc
Nonconvex-nonconcave minimax optimization has received intense attention over the last decade due to its broad applications in machine learning. Most existing algorithms rely on …
Minimax optimization has served as the backbone of many machine learning problems. Although the convergence behavior of optimization algorithms has been extensively studied …
We develop two" Nesterov's accelerated" variants of the well-known extragradient method to approximate a solution of a co-hypomonotone inclusion constituted by the sum of two …
The proximal point algorithm (PPA) is the most widely recognized method for solving inclusion problems and serves as the foundation for many numerical algorithms. Despite this …
Minimax problems have recently attracted a lot of research interests. A few efforts have been made to solve decentralized nonconvex strongly-concave (NCSC) minimax-structured …
L Chen, L Luo - arXiv preprint arXiv:2208.05925, 2022 - arxiv.org
We study the problem of finding a near-stationary point for smooth minimax optimization. The recent proposed extra anchored gradient (EAG) methods achieve the optimal …
Recently, minimax optimization has received renewed focus due to modern applications in machine learning, robust optimization, and reinforcement learning. The scale of these …