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Search Ordering Heuristics for Restarts-based Constraint Solving

机译:搜索RESTARTS的约束求解启动法

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Over the past decade impressive advances have been made in solving Constraint Satisfaction Problems by using of randomization and restarts. In this paper we propose a new class of variable and value ordering heuristics based on learning from no-goods without storing them. We show empirically that these heuristics dramatically improve the performance of restarts-based constraint solving.
机译:在过去的十年中,通过使用随机化并重新启动来解决约束满足问题的令人印象深刻的进步。在本文中,我们提出了一类新的可变和价值订购启发式信息,基于无货的学习而不存储它们。我们凭经验展示了这些启发式大大提高了基于重启的约束求解的性能。

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