AAAI Publications, Twenty-Second International Joint Conference on Artificial Intelligence

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Symmetry Breaking Via LexLeader Feasibility Checkers
Justin Yip, Pascal Van Hentenryck

Last modified: 2011-06-28

Abstract


This paper considers matrix models, a class of CSPs which generally exhibit significant symmetries. It proposed the idea of LexLeader feasibility checkers that verify, during search, whether the current partial assignment can be extended into a canonical solution. The feasibility checkers are based on a novel result by [Katsirelos et al., 2010] on how to check efficiently whether a solution is canonical. The paper generalizes this result to partial assignments, various variable orderings, and value symmetries. Empirical results on 5 standard benchmarks shows that feasibility checkers may bring significant performance gains, when jointly used with DoubleLex or SnakeLex.

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