Zeroth-order algorithms for nonconvex–strongly-concave minimax problems with improved complexities

In this paper, we study zeroth-order algorithms for minimax optimization problems that are nonconvex in one variable and strongly-concave in the other variable. Such minimax optimization problems have attracted significant attention lately due to their applications in modern machine learning tasks....

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Bibliographic Details

Published in:	Journal of global optimization Vol. 87; no. 2-4; pp. 709 - 740
Main Authors:	Wang, Zhongruo, Balasubramanian, Krishnakumar, Ma, Shiqian, Razaviyayn, Meisam
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2023 Springer
Subjects:	Algorithms Comparative analysis Computer Science Machine learning Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Real Functions Stochastic algorithms Oracle complexity Gradient descent ascent Minimax problem Zeroth-order algorithms
ISSN:	0925-5001, 1573-2916
Online Access:	Get full text
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