A novel neural network model for solving a class of nonlinear semidefinite programming problems

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

卷:338

Article

Nikseresht, Asiye¹  Nazemi, Alireza¹ 

[1] Shahrood Univ Technol, Fac Math Sci, POB 3619995161-316, Shahrood, Iran

关键词: Neural network;    Nonlinear semidefinite programming;    Convex optimization;    Stability;    Convergence;   

DOI  :  10.1016/j.cam.2018.01.023

来源: Elsevier

【 摘 要 】

In this paper, we describe a dynamic optimization technique for solving a class of nonlinear semidefinite programming based on Karush-Kuhn-Tucker optimality conditions. By employing Lyapunov function approach, it is investigated that the suggested neural network is stable in the sense of Lyapunov and globally convergent to an exact optimal solution of the original problem. The effectiveness of the proposed method is demonstrated by two numerical simulations. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free