异质性和几何对随机可满足性证明复杂性的影响

IF 0.9 3区 数学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Thomas Bläsius, Tobias Friedrich, Andreas Göbel, Jordi Levy, Ralf Rothenberger
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引用次数: 1

摘要

可满足性被认为是典型的NP完全问题,在理论上被用作硬度约简的起点,而在实践中,启发式SAT求解算法可以非常有效地求解大规模工业SAT实例。理论与实践之间的这种差异被认为是工业SAT实例的固有特性使它们易于处理的结果。两个特征属性似乎在大多数现实世界的SAT实例中普遍存在,异质性程度分布和局部性。为了理解这两个性质对SAT的影响,我们研究了允许控制异质性和局域性的随机SAT模型的证明复杂性。我们的研究结果表明,异质性本身并不能使SAT变得容易,因为异质性随机SAT实例具有超多项式分辨率大小。这意味着这些实例对现代SAT求解器的棘手性。相反,基于底层几何的局部性建模导致在多项式时间内可以找到较小的不满足子公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The impact of heterogeneity and geometry on the proof complexity of random satisfiability
Abstract Satisfiability is considered the canonical NP‐complete problem and is used as a starting point for hardness reductions in theory, while in practice heuristic SAT solving algorithms can solve large‐scale industrial SAT instances very efficiently. This disparity between theory and practice is believed to be a result of inherent properties of industrial SAT instances that make them tractable. Two characteristic properties seem to be prevalent in the majority of real‐world SAT instances, heterogeneous degree distribution and locality. To understand the impact of these two properties on SAT, we study the proof complexity of random ‐SAT models that allow to control heterogeneity and locality. Our findings show that heterogeneity alone does not make SAT easy as heterogeneous random ‐SAT instances have superpolynomial resolution size. This implies intractability of these instances for modern SAT‐solvers. In contrast, modeling locality with underlying geometry leads to small unsatisfiable subformulas, which can be found within polynomial time.
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来源期刊
Random Structures & Algorithms
Random Structures & Algorithms 数学-计算机:软件工程
CiteScore
2.50
自引率
10.00%
发文量
56
审稿时长
>12 weeks
期刊介绍: It is the aim of this journal to meet two main objectives: to cover the latest research on discrete random structures, and to present applications of such research to problems in combinatorics and computer science. The goal is to provide a natural home for a significant body of current research, and a useful forum for ideas on future studies in randomness. Results concerning random graphs, hypergraphs, matroids, trees, mappings, permutations, matrices, sets and orders, as well as stochastic graph processes and networks are presented with particular emphasis on the use of probabilistic methods in combinatorics as developed by Paul Erdõs. The journal focuses on probabilistic algorithms, average case analysis of deterministic algorithms, and applications of probabilistic methods to cryptography, data structures, searching and sorting. The journal also devotes space to such areas of probability theory as percolation, random walks and combinatorial aspects of probability.
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