Soraya Ezazipour

Soraya Ezazipour
Oklahoma State University - Stillwater | Oklahoma State

PHD

About

6
Publications
497
Reads
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53
Citations
Citations since 2017
3 Research Items
39 Citations
2017201820192020202120222023051015
2017201820192020202120222023051015
2017201820192020202120222023051015
2017201820192020202120222023051015
Additional affiliations
September 2007 - June 2010
Tarbiat Modares University
Position
  • Master's Student

Publications

Publications (6)
Article
Full-text available
In this paper, a projection-based recurrent neural network is proposed to solve convex quadratic bilevel programming problems (CQBPP). The Karush–Kuhn–Tucker optimal conditions (KKT) of the lower level problem are used to obtain identical one-level optimization problem. A projected dynamical system which its equilibrium point coincides with the glo...
Article
In this paper, a feedback neural network model is proposed to compute the solution of the mathematical programs with equilibrium constraints (MPEC). The MPEC problem is altered into an identical one-level non-smooth optimization problem, then a sequential dynamic scheme that progressively approximates the non-smooth problem is presented. Besides as...
Article
Full-text available
We establish a relationship between general constrained pseudoconvex optimization problems and globally projected dy-namical systems. A corresponding novel neural network model, which is globally convergent and stable in the sense of Lyapunov, is proposed. Both theoretical and numerical approaches are consid-ered. Numerical simulations for three co...
Article
We propose a novel double projection recurrent neural network model for solving pseudomonotone variational inequalities based on a technique of updating the state variable and fixed point theorem. This model is stable in the sense of Lyapunov and globally convergent for problems that satisfy Lipschitz continuity and pseudomonotonicity conditions. T...
Article
In this paper, we propose a projection neural network model for solving nonlinear convex optimization problems with general linear constraints. Compared with the existing neural network models for solving nonlinear optimization problems, the proposed neural network can be applied to solve a broad class of constrained optimization problems such as d...

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