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arXiv cs/0508044cs.LO
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用参数化解界确定无量词Presburger公式

Deciding Quantifier-Free Presburger Formulas Using Parameterized Solution Bounds

Sanjit A. Seshia, Randal E. Bryant

摘要:

给定一个无量词的Presburger算术公式,如果它有一个令人满意的解,则存在一个其大小(以比特为单位)在公式的大小上多项式有界的解。本文考虑一类特殊的无量词Presburger公式,其中大部分线性约束是差分(分离)约束,而非差分约束是稀疏的.据观察,此类事件通常发生在软件验证中。除了传统的公式大小的措施,我们推导出一个新的解决方案的范围内的参数表征的稀疏性的线性约束和非差约束的数量。特别是,我们表明,每个整数变量所需的位数是线性的非差约束的数量和对数的非零系数的数量和大小,但在其他方面是独立的线性约束的总数在公式中。导出的界限可以用于基于在有限域上实例化整数变量并将输入的无量词Presburger公式转换为等可满足的布尔公式的决策过程中,然后使用布尔可满足性求解器来检查。除了我们的主要理论结果,我们讨论了在实践中获得更严格的界限的几个优化。经验证据表明,我们的决策程序可以大大优于其他决策程序。

英文摘要:

Given a formula in quantifier-free Presburger arithmetic, if it has a satisfying solution, there is one whose size, measured in bits, is polynomially bounded in the size of the formula. In this paper, we consider a special class of quantifier-free Presburger formulas in which most linear constraints are difference (separation) constraints, and the non-difference constraints are sparse. This class has been observed to commonly occur in software verification. We derive a new solution bound in terms of parameters characterizing the sparseness of linear constraints and the number of non-difference constraints, in addition to traditional measures of formula size. In particular, we show that the number of bits needed per integer variable is linear in the number of non-difference constraints and logarithmic in the number and size of non-zero coefficients in them, but is otherwise independent of the total number of linear constraints in the formula. The derived bound can be used in a decision procedure based on instantiating integer variables over a finite domain and translating the input quantifier-free Presburger formula to an equi-satisfiable Boolean formula, which is then checked using a Boolean satisfiability solver. In addition to our main theoretical result, we discuss several optimizations for deriving tighter bounds in practice. Empirical evidence indicates that our decision procedure can greatly outperform other decision procedures.

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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