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Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International C Pierre Schaus Conference proc

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,Coupling Different Integer Encodings for SAT,roblems. More recently MaxSAT solvers have appeared that efficiently solve optimisation problems based on SAT. This means that SAT solvers have become a competitive technology for tackling discrete optimisation problems..A challenge in using SAT solvers for discrete optimisation is the many choices
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,Shattering Inequalities for Learning Optimal Decision Trees,s typically have better out-of-sample performance than heuristic approaches such as CART. However, the underlying MIP formulations often suffer from slow runtimes, due to weak linear programming (LP) relaxations. In this paper, we first propose a new MIP formulation for learning optimal decision tre
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Stochastic Decision Diagrams, a variety of discrete optimization and constraint satisfaction problems. SDDs allow one to extend the relaxation techniques of deterministic diagrams to stochastic dynamic programming problems in which optimal controls are state-dependent. In particular, we develop sufficient conditions under which
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,Improving the Robustness of EPS to Solve the TSP, that are consistent with propagation. Then, each waiting worker takes a sub-problem and solves it. The process is repeated until all the sub-problems have been solved. EPS is based on the idea that if there are many sub-problems to solve then the solving times of the workers will be balanced even i
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