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. © 2011 Springer-Verlag.
CITATION STYLE
Liu, L., Ge, R., & Gao, P. (2011). A novel neural network for solving singular nonlinear convex optimization problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7063 LNCS, pp. 554–561). https://doi.org/10.1007/978-3-642-24958-7_64
Mendeley helps you to discover research relevant for your work.