Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22984
Title: Penalty functions with a small penalty parameter
Authors: Rubinov, AM
Yang, XQ 
Bagirov, AM
Keywords: IPH functions
Least exact penalty parameter
Penalty functions
Issue Date: 2002
Publisher: Taylor & Francis
Source: Optimization methods and software, 2002, v. 17, no. 5, p. 931-964 How to cite?
Journal: Optimization methods and software 
Abstract: In this article, we study the nonlinear penalization of a constrained optimization problem and show that the least exact penalty parameter of an equivalent parametric optimization problem can be diminished. We apply the theory of increasing positively homogeneous (IPH) functions so as to derive a simple formula for computing the least exact penalty parameter for the classical penalty function through perturbation function. We establish that various equivalent parametric reformulations of constrained optimization problems lead to reduction of exact penalty parameters. To construct a Lipschitz penalty function with a small exact penalty parameter for a Lipschitz programming problem, we make a transformation to the objective function by virtue of an increasing concave function. We present results of numerical experiments, which demonstrate that the Lipschitz penalty function with a small penalty parameter is more suitable for solving some nonconvex constrained problems than the classical penalty function.
URI: http://hdl.handle.net/10397/22984
ISSN: 1055-6788
DOI: 10.1080/1055678021000066058
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

15
Last Week
0
Last month
0
Citations as of Aug 18, 2017

WEB OF SCIENCETM
Citations

14
Last Week
0
Last month
0
Citations as of Aug 13, 2017

Page view(s)

34
Last Week
4
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.