Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
838231 | Nonlinear Analysis: Real World Applications | 2010 | 7 Pages |
Abstract
In this paper, the optimization techniques for solving a class of non-differentiable optimization problems are investigated. The non-differentiable programming is transformed into an equivalent or approximating differentiable programming. Based on Karush–Kuhn–Tucker optimality conditions and projection method, a neural network model is constructed. The proposed neural network is proved to be globally stable in the sense of Lyapunov and can obtain an exact or approximating optimal solution of the original optimization problem. An example shows the effectiveness of the proposed optimization techniques.
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Authors
Yongqing Yang, Jinde Cao,