A Method for Solving Nonsmooth Pseudoconvex Optimization

Subject Areas : International Journal of Mathematical Modelling & Computations

Maryam Bala Seyed Ghasir ¹ , Aghileh Heydari ² , Mohammad Ali Badamchizadeh ³

1 - Department of Mathematics, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran
2 - Department of Mathematics, Payame Noor University (PNU), P.O. Box 19395-4697, Tehran, Iran
3 - Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

Received: 2021-08-24 Accepted : 2022-01-20 Published : 2022-03-30

Keywords: Optimization, Recurrent neural network, Global convergence, nonsmooth pseudoconvex,

Abstract :

In this paper, a two layer recurrent neural network (RNN) is shown for solving nonsmooth pseudoconvex optimization . First it is proved that the equilibrium point of the proposed neural network (NN) is equivalent to the optimal solution of the orginal optimization problem. Then, it is proved that the state of the proposed neural network is stable in the sense of Lyapunov, and convergent to an exact optimal solution of the original optimization. Finally two examples are given to illustrate the effectiveness of the proposed neural network.