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A projection based recurrent neural network approach to nonconvex optimization
Shenshen Gu, Jiao Peng
School of Mechatronic Engineering and Automation, University of Shanghai, Shanghai 200072
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Funding: Specialized Research Foundation for the Doctoral Program of Higher Education （No.Grant No.: 20113108120010）
Opened online:23 December 2015
Accepted by: none
Citation: Shenshen Gu, Jiao Peng.A projection based recurrent neural network approach to nonconvex optimization[OL]. [23 December 2015] http://en.paper.edu.cn/en_releasepaper/content/4671758
 
 
In this paper, we propose a projection based recurrent neural network for solving nonconvex programming problems subjected to nonlinear equality and bound constraints. The proposed neural network makes use of a gradient projection onto the tagent space of the constraints and the well-known projection theorem. It is shown here that the proposed neural network is stable and globally convergent to an optimal solution within a finite time. Global convergence analysis are established for nonconvex problems. Numerical examples are provided to show the applicability of the proposed neural network. And the performance proved its effective and efficiency.
Keywords:Binary quadratic problem, Recurrent neural network, Projection theorem, Nonconvex optimization
 
 
 

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