A novel neural network for solving singular nonlinear convex optimization problems

Authors:
Lijun Liu;Rendong Ge;Pengyuan Gao
Affiliations:
School of Science, Dalian Nationalities University, Dalian, P.R. China;School of Science, Dalian Nationalities University, Dalian, P.R. China;School of Science, Dalian Nationalities University, Dalian, P.R. China
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

Singular nonlinear convex optimization problems have been received much attention in recent years. Most existing approaches are in the nature of iteration, which is time-consuming and ineffective. Different approaches to deal with such problems are promising. In this paper, a novel neural network model for solving singular nonlinear convex optimization problems is proposed. By using LaSalle's invariance principle, it is shown that the proposed network is convergent which guarantees the effectiveness of the proposed model for solving singular nonlinear optimization problems. Numerical simulation further verified the effectiveness of the proposed neural network model.