Two-timescale recurrent neural networks for distributed minimax optimization
In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex–concave minimax problems subject to linear equality and nonlinear ineq...
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| Published in | Neural networks Vol. 165; pp. 527 - 539 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.08.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2023.06.003 |
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| Abstract | In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex–concave minimax problems subject to linear equality and nonlinear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks. We demonstrate the viability and efficiency of the proposed neural networks in two specific paradigms for Nash-equilibrium seeking in a zero-sum game and distributed constrained nonlinear optimization. |
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| AbstractList | In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex-concave minimax problems subject to linear equality and nonlinear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks. We demonstrate the viability and efficiency of the proposed neural networks in two specific paradigms for Nash-equilibrium seeking in a zero-sum game and distributed constrained nonlinear optimization.In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex-concave minimax problems subject to linear equality and nonlinear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks. We demonstrate the viability and efficiency of the proposed neural networks in two specific paradigms for Nash-equilibrium seeking in a zero-sum game and distributed constrained nonlinear optimization. In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural networks for solving four different types of generally nonlinear convex-concave minimax problems subject to linear equality and nonlinear inequality constraints. We derive sufficient conditions to guarantee the stability and optimality of the neural networks. We demonstrate the viability and efficiency of the proposed neural networks in two specific paradigms for Nash-equilibrium seeking in a zero-sum game and distributed constrained nonlinear optimization. |
| Author | Xia, Zicong Liu, Yang Wang, Jiasen Wang, Jun |
| Author_xml | – sequence: 1 givenname: Zicong surname: Xia fullname: Xia, Zicong organization: School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China – sequence: 2 givenname: Yang orcidid: 0000-0003-3761-0104 surname: Liu fullname: Liu, Yang email: liuyang@zjnu.edu.cn organization: School of Mathematical Sciences, Zhejiang Normal University, Jinhua 321004, China – sequence: 3 givenname: Jiasen surname: Wang fullname: Wang, Jiasen organization: Future Network Research Center, Purple Mountain Laboratories, Nanjing 211111, China – sequence: 4 givenname: Jun orcidid: 0000-0002-1305-5735 surname: Wang fullname: Wang, Jun email: jwang.cs@cityu.edu.hk organization: Department of Computer Science and School of Data Science, City University of Hong Kong, Hong Kong |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37348433$$D View this record in MEDLINE/PubMed |
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| Keywords | Minimax optimization Distributed optimization Recurrent neural networks Neurodynamic optimization |
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| Snippet | In this paper, we present two-timescale neurodynamic optimization approaches to distributed minimax optimization. We propose four multilayer recurrent neural... |
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| SubjectTerms | Distributed optimization Minimax optimization Neurodynamic optimization Recurrent neural networks |
| Title | Two-timescale recurrent neural networks for distributed minimax optimization |
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