Please use this identifier to cite or link to this item:
Title: An interior-point ℓ 1/2-penalty method for inequality constrained nonlinear optimization
Authors: Tian, B
Yang, X 
Meng, K
Keywords: Constraint qualification
Lower-order penalty function
Nonlinear programming
Primal-dual interior-point method
Quadratic relaxation
Issue Date: 2016
Publisher: American Institute of Mathematical Sciences
Source: Journal of industrial and management optimization, 2016, v. 12, no. 3, p. 949-973 How to cite?
Journal: Journal of industrial and management optimization 
Abstract: In this paper, we study inequality constrained nonlinear programming problems by virtue of an ℓ1/2-penalty function and a quadratic relaxation. Combining with an interior-point method, we propose an interior-point ℓ 1/2-penalty method. We introduce different kinds of constraint qualifications to establish the first-order necessary conditions for the quadratically relaxed problem. We apply the modified Newton method to a sequence of logarithmic barrier problems, and design some reliable algorithms. Moreover, we establish the global convergence results of the proposed method. We carry out numerical experiments on 266 inequality constrained optimization problems. Our numerical results show that the proposed method is competitive with some existing interior-point ℓ1-penalty methods in term of iteration numbers and better when comparing the values of the penalty parameter.
ISSN: 1547-5816
EISSN: 1553-166X
DOI: 10.3934/jimo.2016.12.949
Appears in Collections:Journal/Magazine Article

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

Page view(s)

Last Week
Last month
Citations as of Dec 16, 2018

Google ScholarTM



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