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A recurrent neural network for real-time semidefinte programming
journal contribution
posted on 2023-05-16, 19:12 authored by Jiang, D, Wang, JSemidefinite 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.
History
Publication title
IEEE Transactions on Neural NetworksVolume
10Pagination
81-93ISSN
1045-9227Department/School
School of EngineeringPublisher
IEEEPlace of publication
USARepository Status
- Restricted