Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances

Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

Abstract

The min-max optimization problem, also known as the <italic>saddle point problem</italic>, is a classical optimization problem that 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 that 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 (ML), to just name a few.

Original languageEnglish (US)
Article number9186144
Pages (from-to)55-66
Number of pages12
JournalIEEE Signal Processing Magazine
Volume37
Issue number5
DOIs
StatePublished - Sep 2020

Bibliographical note

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© 1991-2012 IEEE.

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