Stochastic perturbation of reduced gradient & GRG methods for nonconvex programming problems

被引：11

作者：

El Mouatasim, Abdelkrim ^{[1
,2
]}

Ellaia, Rachid ^{[1
]}

Souza de Cursi, Eduardo ^{[3
]}

机构：

[1] Mohammed V Univ Agdal, Lab Study & Res Appl Math, LERMA, Mohammadia Sch Engn, Rabat, Morocco

[2] Univ Ibn Zohr, Fac Polydisciplinaire, Ouarzazate, Morocco

[3] INSA Rouen, LOFIMS EA CNRS 3828, St Etienne, France

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2014年 / 226卷

关键词：

Nonconvex programming; Stochastic perturbation; Constraints optimization; Reduced gradient and GRG methods; Numerical computation; GLOBAL OPTIMIZATION; MINIMAX ESTIMATION; BOUNDED PARAMETER; CONSTRAINTS; ALGORITHM; VARIANT;

D O I：

10.1016/j.amc.2013.10.024

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we consider nonconvex differentiable programming under linear and nonlinear differentiable constraints. A reduced gradient and GRG (generalized reduced gradient) descent methods involving stochastic perturbation are proposed and we give a mathematical result establishing the convergence to a global minimizer. Numerical examples are given in order to show that the method is effective to calculate. Namely, we consider classical tests such as the statistical problem, the octagon problem, the mixture problem and an application to the linear optimal control servomotor problem. (C) 2013 Elsevier Inc. All rights reserved.

引用

页码：198 / 211

页数：14

共 28 条

[21] GENERALIZED GRADIENT METHOD FOR OPTIMAL CONTROL PROBLEMS WITH INEQUALITY CONSTRAINTS AND SINGULAR ARCS [J].