Decomposition Method with a Variable Parameter for a Class of Monotone Variational Inequality Problems
S. L. Wang and
L. Z. Liao
Additional contact information
S. L. Wang: Nanjing University
L. Z. Liao: Hong Kong Baptist University
Journal of Optimization Theory and Applications, 2001, vol. 109, issue 2, No 9, 415-429
Abstract:
Abstract In this paper, we focus on a useful modification of the decomposition method by He et al. (Ref. 1). Experience on applications has shown that the number of iterations of the original method depends significantly on the penalty parameter. The main contribution of our method is that we allow the penalty parameter to vary automatically according to some self-adaptive rules. As our numerical simulations indicate, the modified method is more flexible and efficient in practice. A detailed convergence analysis of our method is also included.
Keywords: monotone variational inequalities; decomposition methods; self-adaptive rules (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://link.springer.com/10.1023/A:1017522623963 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:109:y:2001:i:2:d:10.1023_a:1017522623963
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2
DOI: 10.1023/A:1017522623963
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 ().