Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

A branching heuristic for SAT solvers based on complete implication graphs

Abstract : The performance of modern conflict-driven clause learning (CDCL) SAT solvers strongly depends on branching heuristics. State-of-the-art branching heuristics, such as variable state independent decaying sum (VSIDS) and learning rate branching (LRB), are computed and maintained by aggregating the occurrences of the variables in conflicts. However, these heuristics are not sufficiently accurate at the beginning of the search because they are based on very few conflicts. We propose the distance branching heuristic, which, given a conflict, constructs a complete implication graph and increments the score of a variable considering the longest distance between the variable and the conflict rather than the simple presence of the variable in the graph. We implemented the proposed distance branching heuristic in Maple_LCM and Glucose-3.0, two of the best CDCL SAT solvers, and used the resulting solvers to solve instances from the application and crafted tracks of the 2014 and 2016 SAT competitions and the main track of the 2017 SAT competition. The empirical results demonstrate that using the proposed distance branching heuristic in the initialization phase of Maple_LCM and Glucose-3.0 solvers improves performance. The Maple_LCM solver with the proposed distance branching heuristic in the initialization phase won the main track of the 2017 SAT competition.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-u-picardie.archives-ouvertes.fr/hal-03636419
Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : dimanche 10 avril 2022 - 11:39:15
Dernière modification le : mardi 30 août 2022 - 16:52:24

Identifiants

Citation

Fan Xiao, Chu-Min Li, Mao Luo, Felip Manya, Zhipeng Lu, et al.. A branching heuristic for SAT solvers based on complete implication graphs. Science China Information Sciences, Springer, 2019, 62 (7), ⟨10.1007/s11432-017-9467-7⟩. ⟨hal-03636419⟩

Partager

Métriques

Consultations de la notice

28