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Automated Generation of Simplification Rules for SAT and MAXSAT

机译:SAT和MAXSAT的自动生成简化规则

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摘要

Currently best known upper bounds for many NP-hard problems are obtained by using divide-and-conquer (splitting) algorithms. Roughly speaking, there are two ways of splitting algorithm improvement: a more involved case analysis and an introduction of a new simplification rule. It is clear that case analysis can be executed by computer, so it was considered as a machine task. Recently, several programs for automated case analysis were implemented. However, designing a new simplification rule is usually considered as a human task. In this paper we show that designing simplification rules can also be automated. We present several new (previously unknown) automatically generated simplification rules for the SAT and MAXSAT problems. The new approach allows not only to generate simplification rules, but also to find good splittings. To illustrate our technique we present a new algorithm for (n, 3)-MAXSAT that uses both splittings and simplification rules based on our approach and has worst-case running time O(1.2721~NL), where N is the number of variables and L is the length of an input formula. This bound improves the previously known bound O(1.3248~NL) of Bansal and Raman.
机译:目前最佳已知的许多NP硬问题的上限是通过使用分割和征服(分裂)算法而获得的。粗略地说,有两种分裂算法改进方法:更涉及的案例分析和引入新的简化规则。很明显,可以通过计算机执行案例分析,因此它被视为机器任务。最近,实施了一些自动案例分析的程序。但是,设计新的简化规则通常被视为人工任务。在本文中,我们表明设计简化规则也可以自动化。我们提出了几种新(以前未知)自动生成的SAT和MaxSAT问题的简化规则。新方法不仅可以生成简化规则,还可以找到良好的分裂。为了说明我们的技术,我们为(n,3)-maxsat提供了一种新的算法,它根据我们的方法使用分配器和简化规则,并且具有最坏情况的运行时间O(1.2721〜NL),其中n是变量的数量和L是输入公式的长度。这界限改善了Bansal和拉曼的先前已知的o(1.3248〜nl)。

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