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Automated Generation of Simplification Rules for SAT and MAXSAT

机译:自动生成SAT和MAXSAT的简化规则

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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:通过使用分而治之(分裂)算法,可以得到许多NP难题的当前最著名的上限。粗略地讲,有两种方法可以改进算法,分别是:更复杂的案例分析和新的简化规则的介绍。很显然,案例分析可以由计算机执行,因此被认为是机器任务。最近,实施了一些用于自动案例分析的程序。但是,设计新的简化规则通常被认为是一项人工任务。在本文中,我们表明设计简化规则也可以实现自动化。我们为SAT和MAXSAT问题提出了几种新的(以前未知的)自动生成的简化规则。新方法不仅可以生成简化规则,还可以找到良好的分割。为了说明我们的技术,我们提出了一种用于(n,3)-MAXSAT的新算法,该算法基于我们的方法同时使用拆分和简化规则,并且具有最坏情况下的运行时间O(1.2721〜NL),其中N是变量的数量, L是输入公式的长度。此界限改善了Bansal和Raman先前已知的界限O(1.3248〜NL)。

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