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Heuristic and metaheuristic methodsfor computing graph treewidth

Published online by Cambridge University Press:  15 April 2004

François Clautiaux
Affiliation:
Laboratoire HeuDiaSyC, UMR CNRS 6599, UTC, BP 20529, 60205 Compiègne, France; francois.clautiaux@hds.utc.fr., aziz.moukrim@hds.utc.fr., jacques.carlier@hds.utc.fr.
Aziz Moukrim
Affiliation:
Laboratoire HeuDiaSyC, UMR CNRS 6599, UTC, BP 20529, 60205 Compiègne, France; francois.clautiaux@hds.utc.fr., aziz.moukrim@hds.utc.fr., jacques.carlier@hds.utc.fr.
Stéphane Nègre
Affiliation:
Laboratoire de Recherche en Informatique d'Amiens, INSSET, 48 rue Raspail, 02100 St Quentin, France; stephane.negre@insset.u-picardie.fr.
Jacques Carlier
Affiliation:
Laboratoire HeuDiaSyC, UMR CNRS 6599, UTC, BP 20529, 60205 Compiègne, France; francois.clautiaux@hds.utc.fr., aziz.moukrim@hds.utc.fr., jacques.carlier@hds.utc.fr.
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Abstract

The notion of treewidth is of considerable interest in relation to NP-hard problems.Indeed, several studies have shown that the tree-decomposition method can be used to solve many basic optimization problems in polynomialtime when treewidth is bounded, even if, for arbitrary graphs, computingthe treewidth is NP-hard.Several papers present heuristics with computational experiments.For many graphs the discrepancy between the heuristic resultsand the best lower bounds is still very large. The aim of this paper is to propose two new methodsfor computing the treewidth of graphs: a heuristic and a metaheuristic.The heuristic returns good results in a short computation time,whereas the metaheuristic (a Tabu search method)returns the best results known to have been obtained so far for all the DIMACS vertex coloring / treewidth benchmarks (a well-known collection of graphs used for both vertex coloring and treewidth problems.) Our results actually improve on the previous best results for treewidth problems in 53% of the cases. Moreover, we identify properties of the triangulation processto optimize the computing time of our method.

Type
Research Article
Copyright
© EDP Sciences, 2004

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References

E. Aarts and J.K. Lenstra, Local Search in Combinatorial Optimization. Series in Discrete Mathematics and Optimization. John Wiley and Sons (1997).
Ahuja, R., Ergun, O., Orlin, J. and Punnen, A., A survey on very large-scale neighborhood search techniques. Discrete Appl. Math. 123 (2002) 75-102. CrossRef
Arnborg, S., Corneil, D.G. and Proskurowski, A., Complexity of finding embeddings in a k-tree. SIAM J. Alg. Disc. Meth. 8 (1987) 277-284. CrossRef
Arnborg, S. and Proskurowki, A., Characterisation and recognition of partial 3-trees. SIAM J. Alg. Disc. Meth. 7 (1986) 305-314. CrossRef
Bodlaender, H., A linear time algorithm for finding tree-decompositions of small treewidth. SIAM J. Comput. 25 (1996) 1305-1317. CrossRef
Carlier, J. and Lucet, C., A decomposition algorithm for network reliability evaluation. Discrete Appl. Math. 65 (1993) 141-156. CrossRef
F. Clautiaux, J. Carlier, A. Moukrim and S. Nègre, New lower and upper bounds for graph treewidth. WEA 2003, Lect. Notes Comput. Sci. 2647 (2003) 70-80.
Gavril, F., Algorithms for minimum coloring, maximum clique, minimum coloring cliques and maximum independent set of a chordal graph. SIAM J. Comput. 1 (1972) 180-187. CrossRef
F. Glover and M. Laguna, Tabu search. Kluwer Academic Publishers (1998).
M.C. Golumbic, Algorithmic Graph Theory and Perfect Graphs. Academic Press, New York (1980).
Jensen, F., Lauritzen, S. and Olesen, K., Bayesian updating in causal probabilistic networks by local computations. Comput. Statist. Quaterly 4 (1990) 269-282.
D.S. Johnson and M.A. Trick, The Second DIMACS Implementation Challenge: NP-Hard Problems: Maximum Clique, Graph Coloring, and Satisfiability. Series in Discrete Math. Theor. Comput. Sci. Amer. Math. Soc. (1993).
A. Koster, Frequency Assignment, Models and Algorithms. Ph.D. Thesis, Universiteit Maastricht (1999).
Koster, A., Bodlaender, H. and van Hoesel, S., Treewidth: Computational experiments. Fund. Inform. 49 (2001) 301-312.
Lucet, C., Manouvrier, J.F. and Carlier, J., Evaluating network reliability and 2-edge-connected reliability in linear time for bounded pathwidth graphs. Algorithmica 27 (2000) 316-336. CrossRef
C. Lucet, F. Mendes and A. Moukrim, Méthode de décomposition appliquée à la coloration de graphes, in ROADEF (2002).
Robertson, N. and Seymour, P., Graph minors. II. Algorithmic aspects of tree-width. J. Algorithms 7 (1986) 309-322. CrossRef
Rose, D., Triangulated graphs and the elimination process. J. Math. Anal. Appl. 32 (1970) 597-609. CrossRef
D. Rose, A graph-theoretic study of the numerical solution of sparse positive definite systems of linear equations, in Graph Theory and Computing, edited by R.C. Reed. Academic Press (1972) 183-217.
Rose, D., Tarjan, R. and Lueker, G., Algorithmic aspects of vertex elimination on graphs. SIAM J. Comput. 5 (1976) 146-160. CrossRef
Tarjan, R. and Yannakakis, M., Simple linear-time algorithm to test chordality of graphs, test acyclicity of hypergraphs, and selectivity reduce acyclic hypergraphs. SIAM J. Comput. 13 (1984) 566-579. CrossRef