Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Russian doll search with tree decomposition

Abstract : Optimization in graphical models is an important problem which has been studied in many AI frameworks such as weighted CSP, maximum satisfiability or probabilistic networks. By identifying conditionally independent subproblems, which are solved independently and whose optimum is cached, recent Branch and Bound algorithms offer better asymptotic time complexity. But the locality of bounds induced by decomposition often hampers the practical effects of this result because subproblems are often uselessly solved to optimality. Following the Russian Doll Search (RDS) algorithm, a possible approach to overcome this weakness is to (inductively) solve a relaxation of each subproblem to strengthen bounds. The algorithm obtained generalizes both RDS and treedecomposition based algorithms such as BTD or AND-OR Branch and Bound. We study its efficiency on different problems, closing a very hard frequency assignment instance which has been open for more than 10 years.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

Littérature citée [6 références]  Voir  Masquer  Télécharger

https://hal.inrae.fr/hal-02755904
Déposant : Migration Prodinra <>
Soumis le : mercredi 3 juin 2020 - 22:20:23
Dernière modification le : mercredi 14 octobre 2020 - 03:56:34

Fichier

Russian doll search with tree ...
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Collections

Citation

Marti Sanchez, David Allouche, Simon de Givry, Thomas Schiex. Russian doll search with tree decomposition. 21st International Joint Conference on Artificial Intelligence, Jul 2009, Pasadena, United States. pp.6. ⟨hal-02755904⟩

Partager

Métriques

Consultations de la notice

21

Téléchargements de fichiers

11