Decomposition is a powerful technique for reducing the size of a backtracking search tree. However, when solving constraint optimization problems (COP's) the standard technique of invoking a separate recursion to solve each independent component can significantly reduce the strength of the bounds that can be applied when using branch and bound techniques. In this paper we present a new search algorithm that can obtain many of the computational benefits of decomposition without having to resort to separate recursions. That is, the algorithm explores a standard OR tree not an AND-OR tree. In this way incremental information gathered from any component can be immediately applied to improve the bounding information for all of the other components. We also discuss how caching and local propagation can be combined with our approach and finally test our methods empirically to verify their potential. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Kitching, M., & Bacchus, F. (2008). Exploiting decomposition in constraint optimization problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5202 LNCS, pp. 478–492). https://doi.org/10.1007/978-3-540-85958-1_32
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