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A recurrent neural network for real-time semidefinte programming


Jiang, D and Wang, J, A recurrent neural network for real-time semidefinte programming, IEEE Transactions on Neural Networks, 10, (1) pp. 81-93. ISSN 1045-9227 (1999) [Refereed Article]

DOI: doi:10.1109/72.737496


Semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. This paper proposes a novel recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal problem and the corresponding dual problem. Then a dynamical system is constructed to drive the duality gap to zero exponentially along any trajectory by modifying the gradient of the auxiliary cost function. Furthermore, a subsystem is developed to circumvent in the computation of matrix inverse, so that the resulting overall dynamical system can be realized using a recurrent neural network. The architecture of the resulting neural network is discussed. The operating characteristics and performance of the proposed approach are demonstrated by means of simulation results. © 1999 IEEE.

Item Details

Item Type:Refereed Article
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Manufacturing
Objective Group:Computer, electronic and communication equipment
Objective Field:Integrated circuits and devices
UTAS Author:Jiang, D (Dr Danchi Jiang)
ID Code:44427
Year Published:1999
Web of Science® Times Cited:9
Deposited By:Engineering
Deposited On:2007-05-23
Last Modified:2007-05-23

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