Eliminating the Computation of Strongly Connected Components in Generalized Arc Consistency Algorithm for AllDifferent Constraint
Eliminating the Computation of Strongly Connected Components in Generalized Arc Consistency Algorithm for AllDifferent Constraint
Luhan Zhen, Zhanshan Li, Yanzhi Li, Hongbo Li
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 2049-2057.
https://doi.org/10.24963/ijcai.2023/228
AllDifferent constraint is widely used in Constraint Programming to model real world problems. Existing Generalized Arc Consistency (GAC) algorithms map an AllDifferent constraint onto a bipartite graph and utilize the structure of Strongly Connected Components (SCCs) in the graph to filter values. Calculating SCCs is time-consuming in the existing algorithms, so we propose a novel GAC algorithm for AllDifferent constraint in this paper, which eliminates the computation of SCCs. We prove that all redundant edges in the bipartite graph point to some alternating cycles. Our algorithm exploits this property and uses a more efficient method to filter values, which is based on breadth-first search. Experimental results on the XCSP3 benchmark suite show that our algorithm considerably outperforms the state-of-the-art GAC algorithms.
Keywords:
Constraint Satisfaction and Optimization: CSO: Constraint satisfaction
Constraint Satisfaction and Optimization: CSO: Constraint programming