Global Convergence of a Robust Smoothing SQP Method for Semi-Infinite Programming

C. Ling, L. Q. Qi, G. L. Zhou and S. Y. Wu
Additional contact information
C. Ling: Zhejiang University of Finance and Economics
L. Q. Qi: City University of Hong Kong, Kowloon Tong
G. L. Zhou: Curtin University of Technology
S. Y. Wu: National Cheng-Kung University

Journal of Optimization Theory and Applications, 2006, vol. 129, issue 1, No 9, 147-164

Abstract: Abstract The semi-infinite programming (SIP) problem is a program with infinitely many constraints. It can be reformulated as a nonsmooth nonlinear programming problem with finite constraints by using an integral function. Due to the nondifferentiability of the integral function, gradient-based algorithms cannot be used to solve this nonsmooth nonlinear programming problem. To overcome this difficulty, we present a robust smoothing sequential quadratic programming (SQP) algorithm for solving the nonsmooth nonlinear programming problem. At each iteration of the algorthm, we need to solve only a quadratic program that is always feasible and solvable. The global convergence of the algorithm is established under mild conditions. Numerical results are given.

Keywords: Smoothing SQP algorithm; semi-infinite programming; integral functions; global convergence (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-006-9049-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:129:y:2006:i:1:d:10.1007_s10957-006-9049-0

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-006-9049-0

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().