On Complexity of the Translational-Cut Algorithm for Convex Minimax Problems

K. A. Ariyawansa and P. L. Jiang
Additional contact information
K. A. Ariyawansa: Washington State University
P. L. Jiang: Delta Dental Plan of Minnesota

Journal of Optimization Theory and Applications, 2000, vol. 107, issue 2, No 2, 223-243

Abstract: Abstract Burke, Goldstein, Tseng, and Ye (Ref. 1) have presented an interesting interior-point algorithm for a class of smooth convex minimax problems. They have also presented a complexity analysis leading to a worst-case bound on the total work necessary to obtain a solution within a prescribed tolerance. In this paper, we present refinements to the analysis of Burke et al. which show that the resulting complexity bound can be worse than those for other algorithms available at the time Ref. 1 was published.

Keywords: complexity; minimax optimization; global Newton method; interior-point methods; analytic centers (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1023/A:1026422013954 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:107:y:2000:i:2:d:10.1023_a:1026422013954

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

DOI: 10.1023/A:1026422013954

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 ().