Accelerated Primal-dual Scheme for a Class of Stochastic Nonconvex-concave Saddle Point Problems

Morteza Boroun, Zeinab Alizadeh, Afrooz Jalilzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Stochastic nonconvex-concave min-max saddle point problems appear in many machine learning and control problems including distributionally robust optimization, generative adversarial networks, and adversarial learning. In this paper, we consider a class of nonconvex saddle point problems where the objective function satisfies the Polyak-Łojasiewicz condition with respect to the minimization variable and it is concave with respect to the maximization variable. The existing methods for solving nonconvex-concave saddle point problems often suffer from slow convergence and/or contain multiple loops. Our main contribution lies in proposing a novel single-loop accelerated primal-dual algorithm with new convergence rate results appearing for the first time in the literature, to the best of our knowledge.

Original languageEnglish (US)
Title of host publication2023 American Control Conference, ACC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350328066
StatePublished - 2023
Event2023 American Control Conference, ACC 2023 - San Diego, United States
Duration: May 31 2023Jun 2 2023

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2023 American Control Conference, ACC 2023
Country/TerritoryUnited States
CitySan Diego

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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