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A Progressive Simplifier for Satisfiability Modulo Theories

机译:用于满足性模型理论的渐进式简化

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In this paper we present a new progressive cooperating sim-plifier for deciding the satisfiability of a quantifier-free formula in the first-order theory of integers involving combinations of sublogics, referred to as Satisfiability Modulo Theories (SMT). Our approach, given an SMT problem, replaces each non-propositional theory atom with a Boolean indicator variable yielding a purely propositional formula to be decided by a SAT solver. Starting with the most abstract representation (the Boolean formula), the solver gradually integrates more complex theory solvers into the working decision procedure. Additionally, we propose a method to simplify "expensive" atoms into suitable conjunctions of "cheaper" theory atoms when conflicts occur. This process considerably increases the efficiency of the overall procedure by reducing the number of calls to the slower theory solvers. This is made possible by adopting our novel inter-logic implication framework, as proposed in this paper. We have implemented these methods in our Ario SMT solver by combining three different theory solvers within a DPLL-style SAT solver: a Unit-Two-Variable-Per-Inequality (UTVPI) solver, an integer linear programming (ILP) solver, and a solver for systems of equalities with uninterpreted functions. The efficiencies of our proposed algorithms are demonstrated and exhaustively investigated on a wide range of benchmarks in hardware and software verification domain. Empirical results are also presented showing the advantages/limitations of our methods over other modern techniques for solving these SMT problems.
机译:在本文中,我们提出了一种新渐进配合的SIM plifier用于决定在涉及sublogics的组合整数的一阶理论自由量词-式的满足性,称为满足性模理论(SMT)。我们的方法,给定一个SMT的问题,替换每个非命题理论原子与布尔指示符变量产生一个纯粹命题公式要由SAT解算器来决定。最抽象表示(布尔公式)开始,逐步求解器集成更复杂的理论求解器进入工作决策程序。此外,我们提出了一种方法,发生冲突时,以简化“昂贵”原子渗入“便宜”的理论原子的合适的连词。这个过程大大减少调用速度较慢的理论求解器的数量增加了整个过程的效率。这是通过采用本发明的新型逻辑间蕴涵框架成为可能,如在本文提出。我们求解由DPLL风格税务总局结合三种不同的理论求解求解器实现在我们ARIO SMT这些方法:求解一个单位,两个变量的每不等式(UTVPI),整数线性规划(ILP)求解器,以及一求解与未解释的功能等式的系统。我们提出的算法的效率被证明和详尽考察了广泛的硬件和软件验证领域的基准。还提出了实证结果显示我们的方法在解决这些问题的SMT等现代技术的优势/局限性。

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