Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 21st International C Bistra Dilkina Conference pro

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Andrea Visentin,Aodh ó Gallchóir,Jens K?rcher,Herbert Meyrpread spectrum/CDMA. The book is arranged into 13 chapters. In chapter 1, some key specifications of 3G WCDMA are described and discussed. These techniques incl978-1-4757-7534-1978-0-306-46999-2Series ISSN 0893-3405

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research21st International C

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,Probabilistic Lookahead Strong Branching via?a?Stochastic Abstract Branching Model,ling an additional SB candidate with the reward in expected tree size reduction. We then leverage the insight from the abstract model to design a new stopping criterion for SB, which fits a distribution to the dual gains and, at each node, dynamically continues or interrupts SB. This algorithm, whic

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,Minimizing the?Cost of?Leveraging Influencers in?Social Networks: IP and?CP Approaches,estigate and compare the efficiency and effectiveness of our approaches, we perform a series of experiments using the existing small instances and a new publicly available benchmark of 14 large instances. Our findings yield new optimal solutions to 185 small instances that were previously unsolved,

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,Learning Deterministic Surrogates for?Robust Convex QCQPs,t we solve two smaller and potentially easier problems in training. The second layer (worst case problem) can be seen as a regularisation approach for predict-and-optimise by fitting to a neighbourhood of problems instead of just a point observation. We motivate a reformulation of the worst-case pro

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SMT-Based Repair of Disjunctive Temporal Networks with Uncertainty: Strong and Weak Controllabilityof temporal networks, namely the Disjunctive Temporal Networks with Uncertainty. We use the Satisfiability Modulo Theory framework to formally encode and solve the problem, and we devise a uniform solution encompassing different “l(fā)evels” of controllability, namely strong and weak. Moreover, we provi

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