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arXiv 2406.01307cs.LO
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关于部分量词消去的高效算法研究

On Efficient Algorithms For Partial Quantifier Elimination

Eugene Goldberg

AI总结:

针对现有部分量词消去(PQE)求解器无法复用学习信息的缺陷,本文提出两项支持信息复用的理论成果,有望像冲突子句学习提升SAT求解效率那样,大幅增强PQE的求解性能。

摘要:

此前我们提出了部分量词消去(Partial Quantifier Elimination,PQE),它是常规量词消去的一种推广,可将公式的一部分从量词作用域中取出。我们将PQE应用于带存在量词的命题逻辑CNF公式。PQE的优势在于,等价性检验、模型检验等诸多问题都可通过PQE求解,且该方法效率极高。当前PQE求解器的主要缺陷是无法复用已学习的信息,原因在于这类求解器基于子句冗余概念,而子句冗余是一种结构属性而非语义属性。本文提出两项重要理论成果,可实现PQE求解器已学习信息的复用,这种复用能大幅提升PQE的效率,其作用堪比冲突子句学习对SAT求解的提升效果。

英文摘要:

Earlier, we introduced Partial Quantifier Elimination (PQE). It is a $\mathit{generalization}$ of regular quantifier elimination where one can take a $\mathit{part}$ of the formula out of the scope of quantifiers. We apply PQE to CNF formulas of propositional logic with existential quantifiers. The appeal of PQE is that many problems like equivalence checking and model checking can be solved in terms of PQE and the latter can be very efficient. The main flaw of current PQE solvers is that they do not $\mathit{reuse}$ learned information. The problem here is that these PQE solvers are based on the notion of clause redundancy and the latter is a $\mathit{structural}$ rather than $\mathit{semantic}$ property. In this paper, we provide two important theoretical results that enable reusing the information learned by a PQE solver. Such reusing can dramatically boost the efficiency of PQE like conflict clause learning boosts SAT solving.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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