Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-08T02:54:27.428Z Has data issue: false hasContentIssue false

Des explications pour reconnaîtreet exploiter les structures cachéesd'un problème combinatoire

Published online by Cambridge University Press:  14 February 2007

Hadrien Cambazard
Affiliation:
École des Mines de Nantes – LINA CNRS FRE 2729, 4 rue Alfred Kastler, BP 20722, 44307 Nantes Cedex 3, France; [email protected]  
Narendra Jussien
Affiliation:
École des Mines de Nantes – LINA CNRS FRE 2729, 4 rue Alfred Kastler, BP 20722, 44307 Nantes Cedex 3, France; [email protected]  
Get access

Abstract

L'identification de structures propres à un problème est souvent une étapeclef pour la conception d'heuristiques de recherche comme pour la compréhension de lacomplexité du problème. De nombreuses approches en Recherche Opérationnelleemploient des stratégies de relaxation ou de décomposition dès lors quecertaines struc-tures idoines ont été identifiées. L'étape suivante est laconception d'algorithmes de résolution qui puissent intégrer à la volée,pendant la résolution, ce type d'information. Cet article propose d'utiliser unsolveur de contraintes à base d'explications pour collecter une informationpertinente sur les structures dynamiques et statiques inhérentes au problème.

Type
Research Article
Copyright
© EDP Sciences, 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

D. Achlioptas, L. Kirousis, E. Kranakis, D. Krizanc, M. Molloy and Y. Stamatiou, Random constraint satisfaction: a more accurate picture, in Proceedings CP 1997, Linz, Austria (1997) 121–135.
Benders, J.F., Partitionning procedures for solving mixed-variables programming problems. Numer. Math. 4 (1962) 238252. CrossRef
C. Bessière, A. Chmeiss and L. Saïs, Neighborhood-based variable ordering heuristics for the constraint satisfaction problem, in Proceeding CP'01, Paphos, Cyprus (2001) 565–569. Short paper.
C. Bessiere and J.C. Regin, MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems, in Proceeding CP'96 (1996) 61–75.
F. Boussemart, F. Hemery, C. Lecoutre and L. Sais, Boosting systematic search by weighting constraints, in Proceedings ECAI'04 (2004) 482–486.
Cabon, B., de Givry, S., Lobjois, L., Schiex, T. and Warners, J.P., Radio Link Frequency Assignment. Constraints 4 (1999) 7989. CrossRef
H. Cambazard, P.-E. Hladik, A.-M. Déplanche, N. Jussien and Y. Trinquet, Decomposition and learning for a real time task allocation problem, in Proceedings CP 2004 (2004) 153–167.
H. Cambazard and N. Jussien, Integrating Benders decomposition within Constraint Programming, in Proceedings CP 2005 (2005) 752–756. Short paper.
G. Cleuziou, L. Martin and C. Vrain, Disjunctive learning with a soft-clustering method, in ILP'03:13th International Conference on Inductive Logic Programming, LNCS, September (2003) 75–92.
Geoffrion, A.M., Generalized Benders Decomposition. J. Optim. Theory Practice 10 (1972) 237260. CrossRef
M. Ghoniem, N. Jussien and J.-D. Fekete, VISEXP: visualizing constraint solver dynamics using explanations, in Proceedings FLAIRS'04, Miami, Florida, USA, May (2004) 263–268.
C.P. Gomes, B.t Selman and N. Crato, Heavy-tailed distributions in combinatorial search, in Proceeding CP 97, Linz, Austria (1997) 121–135.
Haralick, R. and Elliot, G., Increasing tree search efficiency for constraint satisfaction problems. Artificial intelligence 14 (1980) 263313. CrossRef
Hooker, J.N. and Ottosson, G., Logic-based benders decomposition. Math. Program. 96 (2003) 3360. CrossRef
Jain, V. and Grossmann, I.E., Algorithms for hybrid MILP/CP models for a class of optimization problems. Informs J. Comput. 13 (2001) 258276. CrossRef
N. Jussien, The versatility of using explanations within constraint programming. Habilitation thesis, Université de Nantes, France, also available as RR-03-04 research report at École des Mines de Nantes (2003).
N. Jussien and V. Barichard, The PaLM system: explanation-based constraint programming, in Proceedings of TRICS: Techniques foR Implementing Constraint programming Systems, a post-conference workshop of CP 2000, Singapore (2000) 118–133.
N. Jussien, R. Debruyne and P. Boizumault, Maintaining arc-consistency within dynamic backtracking, in Proceedings CP 2000, edited by R. Dechter, Singapore (2000) 249–261.
Jussien, N. and Lhomme, O., Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence 139 (2002) 2145. CrossRef
R. Monasson, R. Zecchina, S. Kirkpatrick, B. Selman and L. Troyanski, Determining computational complexity for characteristic `phase transitions', in Nature 400 (1999) 133–137.
P. Prosser, MAC-CBJ: maintaining arc-consistency with conflict-directed backjumping. Research report 95/177, Department of Computer Science – University of Strathclyde (2005).
P. Prosser, K. Stergiou and T. Walsh, Singleton consistencies, in Proceedings CP 2000, edited by R. Dechter, Singapore (2000) 353–368.
P. Refalo, Impact-based search strategies for constraint programming, in Proceedings CP 2004, Toronto, Canada (2004) 556–571.
J.-C. Régin, A filtering algorithm for constraints of difference in CSPs, in AAAI 94, Twelth National Conference on Artificial Intelligence, Seattle, Washington (1994) 362–367.
R. Williams, C. Gomes and B. Selman, On the connections between backdoors and heavy-tails on combinatorial search, in the International Conference on Theory and Applications of Satisfiability Testing (SAT) (2003).
Ryan Williams, Carla P. Gomes, and Bart Selman. Backdoors to typical case complexity, in Proceedings IJCAI 2003 (2003).