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On the Power of Clause-Learning SAT Solvers with Restarts

机译:重新启动子句学习SAT解算器的功能

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In this work, we improve on existing work that studied the relationship between the proof system of modern SAT solvers and general resolution. Previous contributions such as those by Beame et al (2004), Hertel et al (2008), and Buss et al (2008) demonstrated that variations on modern clause-learning SAT solvers were as powerful as general resolution. However, the models used in these studies required either extra degrees of non-determinism or a preprocessing step that are not utilized by any state-of-the-art SAT solvers in practice. In this paper, we prove that modern SAT solvers that learn asserting clauses indeed p-simulate general resolution without the need for any additional techniques.
机译:在这项工作中,我们对研究现代SAT求解器的证明系统与一般分辨率之间的关系的现有工作进行了改进。先前的贡献,例如Beame等人(2004),Hertel等人(2008)和Buss等人(2008)的研究表明,现代子句学习SAT求解器的变化与通用分辨率一样强大。但是,这些研究中使用的模型需要额外的不确定性程度或预处理步骤,而实际中任何先进的SAT解算器都不会利用这些模型。在本文中,我们证明了学习断言从句的现代SAT求解器确实可以p-模拟一般分辨率,而无需任何其他技术。

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