Computational experience with a SDP-based algorithm for maximum cut with limited unbalance

In the Maximum Cut with Limited Unbalance problem, we want to partition the vertices of a weighted graph into two sets of sizes differing at most by a given threshold B, so that the sum of the weights of the crossing edges is maximum. This problem has been introduced in (Galbiati and Maffioli, Theor...

Full description

Saved in:
Bibliographic Details
Published in:Networks Vol. 55; no. 3; pp. 247 - 255
Main Authors: Galbiati, Giulia, Gualandi, Stefano, Maffioli, Francesco
Format: Journal Article Conference Proceeding
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.05.2010
Wiley
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science; control theory; systems
Exact sciences and technology
Flows in networks. Combinatorial problems
Mathematical programming
network design
Operational research and scientific management
Operational research. Management science
randomized approximation algorithm
semidefinite programming
Theoretical computing
unbalanced maximum cut
randomized approximation algorithm
Partition
Polynomial approximation
Unbalanced conditions
Semi definite programming
Polynomial method
Distributed system
Approximation algorithm
Randomized algorithm
Weighted graph
Polynomial time
semidefinite programming
unbalanced maximum cut
Randomized design
network design
Algorithm analysis
ISSN:0028-3045, 1097-0037
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the Maximum Cut with Limited Unbalance problem, we want to partition the vertices of a weighted graph into two sets of sizes differing at most by a given threshold B, so that the sum of the weights of the crossing edges is maximum. This problem has been introduced in (Galbiati and Maffioli, Theor Comput Sci 385 (2007), 78–87) where polynomial time randomized approximation algorithms are proposed and their performance guarantees are analyzed in the case of non‐negative integer weights. In this article, we present extensive computational experience with these algorithms on a large number of different graphs. We then extend the analysis of these algorithms to integer weights not restricted in sign, and continue the computational testing. It turns out that the approximation ratios obtained are always substantially better than those guaranteed by the theoretical analysis. © 2010 Wiley Periodicals, Inc. NETWORKS 2010
Bibliography:ark:/67375/WNG-J5VJSSMZ-H
istex:2D224F2923E4CE80326DEBE2547A08EAD4E892C1
ArticleID:NET20369
ISSN:0028-3045
1097-0037
DOI:10.1002/net.20369