SAT在公式长度方面的进一步改进

IF 0.8 4区 计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS Information and Computation Pub Date : 2023-10-01 DOI:10.1016/j.ic.2023.105085
Junqiang Peng, Mingyu Xiao
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引用次数: 0

摘要

在本文中,我们证明了一般的CNF可满足性问题可以在O(1.0638L)时间内求解,其中L是输入CNF公式的长度(即公式中的文字总数),这改进了2009年获得的O(1.0652L)的先前结果。采用度量征服法对算法进行了分析。我们的改进主要归功于以下两点:我们仔细设计了分支规则来处理5级和4级变量,以避免以前的瓶颈;我们证明了一些最坏的情况不会总是发生,然后我们可以使用摊销技术来获得进一步的改进。在我们的分析中,我们提供了一些通用的分析框架和关于测度递减的几个下界,以简化论点。这些技术可以用于分析更多基于测量和征服方法的算法。
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Further improvements for SAT in terms of formula length

In this paper, we prove that the general CNF satisfiability problem can be solved in O(1.0638L) time, where L is the length of the input CNF-formula (i.e., the total number of literals in the formula), which improves the previous result of O(1.0652L) obtained in 2009. Our algorithm was analyzed by using the measure-and-conquer method. Our improvements are mainly attributed to the following two points: we carefully design branching rules to deal with degree-5 and degree-4 variables to avoid previous bottlenecks; we show that some worst cases will not always happen, and then we can use an amortized technique to get further improvements. In our analyses, we provide some general frameworks for analysis and several lower bounds on the decreasing of the measure to simplify the arguments. These techniques may be used to analyze more algorithms based on the measure-and-conquer method.

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来源期刊
Information and Computation
Information and Computation 工程技术-计算机:理论方法
CiteScore
2.30
自引率
0.00%
发文量
119
审稿时长
140 days
期刊介绍: Information and Computation welcomes original papers in all areas of theoretical computer science and computational applications of information theory. Survey articles of exceptional quality will also be considered. Particularly welcome are papers contributing new results in active theoretical areas such as -Biological computation and computational biology- Computational complexity- Computer theorem-proving- Concurrency and distributed process theory- Cryptographic theory- Data base theory- Decision problems in logic- Design and analysis of algorithms- Discrete optimization and mathematical programming- Inductive inference and learning theory- Logic & constraint programming- Program verification & model checking- Probabilistic & Quantum computation- Semantics of programming languages- Symbolic computation, lambda calculus, and rewriting systems- Types and typechecking
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