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Revisiting Graph Width Measures for CNF-Encodings

机译:REVISITING CNF编码的图形宽度测量

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摘要

We consider bounded width CNF-formulas where the width is measured by popular graph width measures on graphs associated to CNF-formulas. Such restricted graph classes, in particular those of bounded treewidth, have been extensively studied for their uses in the design of algorithms for various computational problems on CNF-formulas. Here we consider the expressivity of these formulas in the model of clausal encodings with auxiliary variables. We first show that bounding the width for many of the measures from the literature leads to a dramatic loss of expressivity, restricting the formulas to such of low communication complexity. We then show that the width of optimal encodings with respect to different measures is strongly linked: there are two classes of width measures, one containing primal treewidth and the other incidence cliquewidth, such that in each class the width of optimal encodings only differs by constant factors. Moreover, between the two classes the width differs at most by a factor logarithmic in the number of variables. Both these results are in stark contrast to the setting without auxiliary variables where all width measures we consider here differ by more than constant factors and in many cases even by linear factors.
机译:我们考虑有界宽度CNF-公式,其中宽度通过与CNF-Formulas相关联的曲线图上的流行图宽度测量测量。这种限制图表类别,特别是有界树木宽度的类别已经广泛研究了它们在CNF-Formulas上各种计算问题的算法设计中的用途。在这里,我们考虑这些公式在具有辅助变量的子宫内编码模型中的表达性。我们首先表明,对于来自文献的许多措施的宽度来说,宽度导致表达性的急剧丧失,限制了这种低通信复杂性的公式。然后,我们表明,关于不同措施的最佳编码宽度是强烈的链接:有两类宽度测量,一个含有原始树木宽度和另一个入射素,使得在每个阶级的最佳编码宽度仅不同因素。此外,在两个类之间,宽度最多由变量数量的因子对数而异。这两个结果都与没有辅助变量的设置鲜明对比,其中我们考虑的所有宽度措施都不同于恒定因素,并且在许多情况下甚至通过线性因素。

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