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An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems: An accelerated first-order regularized momentum descent...: H. Zhang, Z. Xu.

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Title: An accelerated first-order regularized momentum descent ascent algorithm for stochastic nonconvex-concave minimax problems: An accelerated first-order regularized momentum descent...: H. Zhang, Z. Xu.
Authors: Zhang, Huiling1 (AUTHOR), Xu, Zi1,2 (AUTHOR) xuzi@shu.edu.cn
Source: Computational Optimization & Applications. Mar2025, Vol. 90 Issue 2, p557-582. 26p.
Subjects: Computational mathematics, Computational statistics, Machine learning, Signal processing, Algorithms, Nonconvex programming
Abstract: Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose an accelerated first-order regularized momentum descent ascent algorithm (FORMDA) for solving stochastic nonconvex-concave minimax problems. The iteration complexity of the algorithm is proved to be O ~ (ε - 6.5) to obtain an ε -stationary point, which achieves the best-known complexity bound for single-loop algorithms to solve the stochastic nonconvex-concave minimax problems under the stationarity of the objective function. [ABSTRACT FROM AUTHOR]
Copyright of Computational Optimization & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Stochastic nonconvex minimax problems have attracted wide attention in machine learning, signal processing and many other fields in recent years. In this paper, we propose an accelerated first-order regularized momentum descent ascent algorithm (FORMDA) for solving stochastic nonconvex-concave minimax problems. The iteration complexity of the algorithm is proved to be O ~ (ε - 6.5) to obtain an ε -stationary point, which achieves the best-known complexity bound for single-loop algorithms to solve the stochastic nonconvex-concave minimax problems under the stationarity of the objective function. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computational Optimization & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10589-024-00638-9
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      – SubjectFull: Computational statistics
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      – SubjectFull: Machine learning
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      – SubjectFull: Algorithms
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              Text: Mar2025
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