On improving convex quadratic programming relaxation for the quadratic assignment problem

Yong Xia () and Wajeb Gharibi ()
Additional contact information
Yong Xia: Beihang University
Wajeb Gharibi: Jazan University

Journal of Combinatorial Optimization, 2015, vol. 30, issue 3, No 15, 647-667

Abstract: Abstract Relaxation techniques play a great role in solving the quadratic assignment problem, among which the convex quadratic programming bound (QPB) is competitive with existing bounds in the trade-off between cost and quality. In this article, we propose two new lower bounds based on QPB. The first dominates QPB at a high computational cost, which is shown equivalent to the recent second-order cone programming bound. The second is strictly tighter than QPB in most cases, while it is solved as easily as QPB.

Keywords: Quadratic assignment problem; Lower bound; Capacitated transportation problem; Quadratic programming; 90C10; 90C20; 90C26 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10878-013-9655-3 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:jcomop:v:30:y:2015:i:3:d:10.1007_s10878-013-9655-3

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-013-9655-3

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

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