Exploiting Decomposition in Constraint Optimization Problems

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.