A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

Title:

A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints

Creator:

Ezazipour, Soraya and Golbabai, Ahmad

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:9d4266f0-55e5-4869-96ab-3b001a51f35d
uuid:9d4266f0-55e5-4869-96ab-3b001a51f35d
doi:10.14736/kyb-2020-3-0383

Subject:

neural network, mathematical programming with equilibrium constraints, asymptotically stability, and globally convergence

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper introduces a neurodynamics optimization model to compute the solution of mathematical programming with equilibrium constraints (MPEC). A smoothing method based on NPC-function is used to obtain a relaxed optimization problem. The optimal solution of the global optimization problem is estimated using a new neurodynamic system, which, in finite time, is convergent with its equilibrium point. Compared to existing models, the proposed model has a simple structure, with low complexity. The new dynamical system is investigated theoretically, and it is proved that the steady state of the proposed neural network is asymptotic stable and global convergence to the optimal solution of MPEC. Numerical simulations of several examples of MPEC are presented, all of which confirm the agreement between the theoretical and numerical aspects of the problem and show the effectiveness of the proposed model. Moreover, an application to resource allocation problem shows that the new method is a simple, but efficient, and practical algorithm for the solution of real-world MPEC problems.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

383-409

Source:

Kybernetika | 2020 Volume:56 | Number:3

Harvested from:

CDK

Metadata only:

false