Solving a class of non-convex quadratic problems based on generalized KKT conditions and neurodynamic optimization technique

Title:

Solving a class of non-convex quadratic problems based on generalized KKT conditions and neurodynamic optimization technique

Creator:

Malek, Alaeddin and Hosseinipour-Mahani, Najmeh

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:196ffe99-c68c-4ceb-9737-e517bb3360c8
uuid:196ffe99-c68c-4ceb-9737-e517bb3360c8

Subject:

non-convex quadratic optimization, recurrent neural network model, global optimality conditions, and global convergence

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

n this paper, based on a generalized Karush-Kuhn-Tucker (KKT) method a modified recurrent neural network model for a class of non-convex quadratic programming problems involving a so-called Z-matrix is proposed. The basic idea is to express the optimality condition as a mixed nonlinear complementarity problem. Then one may specify conditions for guaranteeing the global solutions of the original problem by using results from the S-lemma. This process is proved by building up a dynamic system from the optimality condition whose equilibrium point is exactly the solution of the mixed nonlinear complementarity problem. By the study of the resulting dynamic system it is shown that under given assumptions, steady states of the dynamic system are stable. Numerical simulations and comparisons with the other methods are presented to illustrate the efficiency of the practical technique that is proposed in this paper.

Language:

English

Rights:

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

Coverage:

[890]-908

Source:

Kybernetika | 2015 Volume:51 | Number:5

Harvested from:

CDK

Metadata only:

false