A novel neural network for solving semidefinite programming problems with some applications

In this paper, we present a neural network model for solving semidefinite programming. We show that the equilibrium point of the proposed neural network and the KKT point of the semidefinite programming are equal. By employing Lyapunov function approach, it is also shown that the suggested neural ne...

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Bibliographic Details
Published inJournal of computational and applied mathematics Vol. 350; pp. 309 - 323
Main Authors Nikseresht, Asiye, Nazemi, Alireza
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.04.2019
Subjects
Convergent
Convex programming
Neural network
Semidefinite programming
Stability
Convergent
Semidefinite programming
Stability
Neural network
Convex programming
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ISSN0377-0427
1879-1778
DOI10.1016/j.cam.2018.10.025

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Summary:In this paper, we present a neural network model for solving semidefinite programming. We show that the equilibrium point of the proposed neural network and the KKT point of the semidefinite programming are equal. By employing Lyapunov function approach, it is also shown that the suggested neural network model is stable in the sense of Lyapunov and globally convergent to an exact optimal solution of the original problem. Some applications of the proposed scheme are stated by modeling of some important problems as semidefinite programming problems. Numerical simulations verify the obtained theoretical results.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2018.10.025