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A New Optimization Method Based on Dynamic Neural Networks for Solving Non-convex Quadratic Constrained Optimization Problems

Publish place: Control and Optimization in Applied Mathematics، Vol: 7، Issue: 2
Publish Year: 1401
Type: Journal paper
Language: English
View: 376

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Link to this Paper:
https://civilica.com/doc/1605881

Document National Code:

JR_COAM-7-2_002

Index date: 19 February 2023

A New Optimization Method Based on Dynamic Neural Networks for Solving Non-convex Quadratic Constrained Optimization Problems abstract

This paper presents a capable recurrent neural network, the so-called µRNN for solving a class of non-convex quadratic programming problems‎. ‎Based on the optimality conditions we construct a new recurrent neural network (µRNN)‎, ‎which has a simple structure and its capability is preserved‎. ‎The proposed neural network model is stable in the sense of Lyapunov and converges to the exact optimal solution of the original problem‎. ‎In a particular case‎, ‎the optimality conditions of the problem become necessary and sufficient‎. ‎Numerical experiments and comparisons with some existing algorithms are presented to illustrate the theoretical results and show the efficiency of the proposed network.

A New Optimization Method Based on Dynamic Neural Networks for Solving Non-convex Quadratic Constrained Optimization Problems Keywords:

Quadratic programming‎ , ‎Recurrent neural network‎ , ‎Non-convex optimization

A New Optimization Method Based on Dynamic Neural Networks for Solving Non-convex Quadratic Constrained Optimization Problems authors

Kobra Mohammadsalahi

Department of Mathematics‎, ‎Tabriz Branch‎, ‎Islamic Azad University‎, ‎Tabriz‎, ‎Iran‎.

Farzin Modarres Khiyabani

Department of Mathematics‎, ‎Tabriz Branch‎, ‎Islamic Azad University‎, ‎Tabriz‎, ‎Iran‎.

Nima Azarmir Shotorbani

Department of Mathematics‎, ‎Tabriz Branch‎, ‎Islamic Azad University‎, ‎Tabriz‎, ‎Iran‎.

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