SAT实例分类的结构特征

Q1 Mathematics
Carlos Ansótegui , Maria Luisa Bonet , Jesús Giráldez-Cru , Jordi Levy
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引用次数: 17

摘要

组合方法在SAT求解中的成功依赖于这样一种观察:不同的SAT求解者可能会根据他们试图解决的SAT实例的类别而显著改变他们的表现。在这些方法中,使用问题的一组特征来构建预测模型,该模型将实例分类,并计算最快的算法来解决每个问题。因此,用于构建这些分类器的特征集起着至关重要的作用。传统上,组合SAT解包括问题的结构和难度的特征。最近,有一些人试图更好地描述工业SAT实例的结构。本文利用工业SAT实例的一些结构特征来构建工业SAT实例族的分类器。即无标度结构、群落结构和自相似结构。首先,我们通过将这些分类器与组合SAT求解方法中常用的其他SAT特征集进行比较来衡量这些分类器的有效性。然后,我们评估了该结构特征集在实际投资组合SAT求解器中的性能。最后,我们分析了这些特征在分类器上的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structure features for SAT instances classification

The success of portfolio approaches in SAT solving relies on the observation that different SAT solvers may dramatically change their performance depending on the class of SAT instances they are trying to solve. In these approaches, a set of features of the problem is used to build a prediction model, which classifies instances into classes, and computes the fastest algorithm to solve each of them. Therefore, the set of features used to build these classifiers plays a crucial role. Traditionally, portfolio SAT solvers include features about the structure of the problem and its hardness.

Recently, there have been some attempts to better characterize the structure of industrial SAT instances. In this paper, we use some structure features of industrial SAT instances to build some classifiers of industrial SAT families of instances. Namely, they are the scale-free structure, the community structure and the self-similar structure. First, we measure the effectiveness of these classifiers by comparing them to other sets of SAT features commonly used in portfolio SAT solving approaches. Then, we evaluate the performance of this set of structure features when used in a real portfolio SAT solver. Finally, we analyze the relevance of these features on the analyzed classifiers.

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来源期刊
Journal of Applied Logic
Journal of Applied Logic COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
1.13
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Cessation.
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