A continuous-time neurodynamic algorithm for distributed nonconvex nonsmooth optimization problems with affine equality and nonsmooth convex inequality constraints
In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the...
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| Published in: | Neurocomputing (Amsterdam) Vol. 507; pp. 383 - 396 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.10.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is bounded and globally exists, and will converge to the critical point set of distributed problems in a finite time. Compared with other DNNO algorithms, distributed neurodynamic algorithm has a lower dimension and does not need to satisfy the assumption that the feasible region is bounded. Finally, a series of numerical examples are given to verify the effectiveness of the proposed algorithm. |
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| AbstractList | In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is bounded and globally exists, and will converge to the critical point set of distributed problems in a finite time. Compared with other DNNO algorithms, distributed neurodynamic algorithm has a lower dimension and does not need to satisfy the assumption that the feasible region is bounded. Finally, a series of numerical examples are given to verify the effectiveness of the proposed algorithm. |
| Author | He, Xing Yang, Jianyu |
| Author_xml | – sequence: 1 givenname: Jianyu surname: Yang fullname: Yang, Jianyu organization: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China – sequence: 2 givenname: Xing surname: He fullname: He, Xing email: hexingdoc@swu.edu.cn organization: Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China |
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| Keywords | Distributed nonsmooth nonconvex optimization Continuous-time neurodynamic algorithm Finite-time convergence |
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| SubjectTerms | Continuous-time neurodynamic algorithm Distributed nonsmooth nonconvex optimization Finite-time convergence |
| Title | A continuous-time neurodynamic algorithm for distributed nonconvex nonsmooth optimization problems with affine equality and nonsmooth convex inequality constraints |
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