
AbstractIn 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
Combinatorial optimization, 000, 1020 Informatik, network design, semidefinite programming, randomized approximation algorithm, [INFO.INFO-RO] Computer Science [cs]/Operations Research [math.OC], 004, 101015 Operations Research, Bilevel programming, AUT, 101015 Operations research, Recherche opérationnelle, 1020 Computer Sciences, Network pricing
Combinatorial optimization, 000, 1020 Informatik, network design, semidefinite programming, randomized approximation algorithm, [INFO.INFO-RO] Computer Science [cs]/Operations Research [math.OC], 004, 101015 Operations Research, Bilevel programming, AUT, 101015 Operations research, Recherche opérationnelle, 1020 Computer Sciences, Network pricing
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 793 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 0.1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 0.1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
