Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
Abstract
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games. Given a class of objective functions, the goal is to find a value for the argument which leads to a small objective value even for the worst case function in the given class. Min-max optimization problems have recently become very popular in a wide range of signal and data processing applications such as fair beamforming, training generative adversarial networks (GANs), and robust machine learning, to just name a few. The overarching goal of this article is to provide a survey of recent advances for an important subclass of min-max problem, where the minimization and maximization problems can be non-convex and/or non-concave. In particular, we will first present a number of applications to showcase the importance of such min-max problems; then we discuss key theoretical challenges, and provide a selective review of some exciting recent theoretical and algorithmic advances in tackling non-convex min-max problems. Finally, we will point out open questions and future research directions.
Cite
@article{arxiv.2006.08141,
title = {Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances},
author = {Meisam Razaviyayn and Tianjian Huang and Songtao Lu and Maher Nouiehed and Maziar Sanjabi and Mingyi Hong},
journal= {arXiv preprint arXiv:2006.08141},
year = {2021}
}