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A Faster Counterexample Minimization Algorithm Based on Refutation Analysis

机译:基于引用分析的快速反例最小化算法

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It is a hot research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. The BFL algorithm is the most effective counterexample minimization algorithm compared to all other approaches. But its time overhead is very large due to one call to SAT solver for each candidate variable to be eliminated. The key to reduce time overhead is to eliminate multiple variables simultaneously. Therefore, we propose a faster counterexample minimization algorithm based on refutation analysis in this paper. We perform refutation analysis on those UNSAT instances of BFL, to extract the set of variables that lead to UNSAT. All variables not belong to this set can be eliminated simultaneously as irrelevant variables. Thus we can eliminate multiple variables with only one call to SAT solver. Theoretical analysis and experiment result shows that, our algorithm can be 2 to 3 orders of magnitude faster than existing BFL algorithm, and with only minor lost in counterexample minimization ability.
机译:从反例中消除无关的变量,使其更易于理解,这是一个热门的研究主题。与所有其他方法相比,BFL算法是最有效的反例最小化算法。但是由于要消除每个候选变量的一个SAT求解器调用,其时间开销非常大。减少时间开销的关键是同时消除多个变量。因此,本文提出了一种基于反驳分析的更快的反例最小化算法。我们对BFL的UNSAT实例进行反驳分析,以提取导致UNSAT的变量集。可以同时消除所有不属于此集合的变量作为无关变量。因此,仅需调用SAT解算器即可消除多个变量。理论分析和实验结果表明,我们的算法可以比现有的BFL算法快2到3个数量级,并且在反例最小化能力方面仅损失了很小的一部分。

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