Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Tree search and ARC consistency in constraint satisfaction algorithms
Search in Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Arc-consistency and arc-consistency again
Artificial Intelligence
Maintaining reversible DAC for Max-CSP
Artificial Intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Lower Bounds for Non-binary Constraint Optimization Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Opportunistic Specialization in Russian Doll Search
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Pairwise decomposition for combinatorial optimization in graphical models
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
| Hi-index | 0.01 |
Variable elimination is the basic step of Adaptive Consistency [4]. It transforms the problem into an equivalent one, having one less variable. Unfortunately, there are many classes of problems for which it is infeasible, due to its exponential space and time complexity. However, by restricting variable elimination so that only low arity constraints are processed and recorded, it can be effectively combined with search, because the elimination of variables, reduces the search tree size. In this paper we introduce VarElimSearch(S, k), a hybrid metaalgorithm that combines search and variable elimination. The parameter S names the particular search procedure and k controls the tradeoff between the two strategies. The algorithm is space exponential in k. Regarding time, we show that its complexity is bounded by k and a structural parameter from the constraint graph. We also provide experimental evidence that the hybrid algorithm can outperform state-of-the-art algorithms in binary sparse problems. Experiments cover the tasks of finding one solution and the best solution (Max-CSP). Specially in the Max-CSP case, the advantage of our approach can be overwhelming.