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We present in this paper a new complete method for distributed constraint optimization. This is a utility-propagation method, inspired by the sum-product algorithm. The original algorithm requires fixed message sizes, linear memory, and is time-linear in the size of the problem. However, it is correct only for tree-shaped constraint networks. In this paper, we show how to extend the algorithm to arbitrary topologies using cycle cutsets, while preserving the linear message size and memory requirements. We present some preliminary experimental results on randomly generated problems. The algorithm is formulated for optimization problems, but can be easily applied to satisfaction problems as well.