Randomized rounding algorithms for large scale unsplittable flow problems

François Lamothe (), Emmanuel Rachelson, Alain Haït, Cedric Baudoin and Jean-Baptiste Dupé
Additional contact information
François Lamothe: Université de Toulouse
Emmanuel Rachelson: Université de Toulouse
Alain Haït: Université de Toulouse
Cedric Baudoin: Thalès Alenia Space
Jean-Baptiste Dupé: Centre national d’études spatiales (CNES)

Journal of Heuristics, 2021, vol. 27, issue 6, No 5, 1110 pages

Abstract: Abstract Unsplittable flow problems cover a wide range of telecommunication and transportation problems and their efficient resolution is key to a number of applications. In this work, we study algorithms that can scale up to large graphs and important numbers of commodities. We present and analyze in detail a heuristic based on the linear relaxation of the problem and randomized rounding. We provide empirical evidence that this approach is competitive with state-of-the-art resolution methods either by its scaling performance or by the quality of its solutions. We provide a variation of the heuristic which has the same approximation factor as the state-of-the-art approximation algorithm. We also derive a tighter analysis for the approximation factor of both the variation and the state-of-the-art algorithm. We introduce a new objective function for the unsplittable flow problem and discuss its differences with the classical congestion objective function. Finally, we discuss the gap in practical performance and theoretical guarantees between all the aforementioned algorithms.

Keywords: Unsplittable flows; Randomized rounding; Heuristic; Approximation algorithm (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10732-021-09478-w 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:joheur:v:27:y:2021:i:6:d:10.1007_s10732-021-09478-w

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10732

DOI: 10.1007/s10732-021-09478-w

Access Statistics for this article

Journal of Heuristics is currently edited by Manuel Laguna

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