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Distributed constraint optimization (DCOP) is a model where several agents coordinate with each other to take on values so as to minimize the sum of the resulting constraint costs, which are dependent on the values of the agents. This model is becoming popular for formulating and solving multi-agent coordination problems. As a result, researchers have developed a class of DCOP algorithms that use search techniques. Since solving DCOP problems optimally is NP-hard, solving large problems efficiently becomes an issue. In this book, I show how one can speed up DCOP search algorithms by applying insights gained from centralized search algorithms, specifically by using an appropriate search strategy; by sacrificing solution optimality; by using more memory; and by reusing information gained from solving similar DCOP problems.