arXiv:2412.16226cs.LOcs.AI2024-12AAAI被引 4

提出新方法高效求解带量词的线性与多项式数理公式

Quantified Linear and Polynomial Arithmetic Satisfiability via Template-based Skolemization

  • 用模板法自动合成线性/多项式斯科伦函数消除量词
  • 在LRA和NRA基准上解决实例数和运行时间均优于现有SMT求解器
  • 理论保证正确性、半完备性,且复杂度为次指数时间

检查线性实数算术(LRA)和非线性实数算术(NRA)公式的可满足性问题具有广泛的应用,尤其在人工智能逻辑、程序分析等领域至关重要。尽管已有大量工作针对无量词LRA和NRA公式的可满足性检查,但带量词的LRA和NRA公式的可满足性检查仍是一个重大挑战,主要瓶颈在于计算昂贵的量词消去步骤。本文提出一种新的高效量词消去方法,采用基于模板的斯科伦化策略,自动合成线性/多项式斯科伦函数以消除公式中的量词。核心技术基于代数几何中的正定定理,可高效处理多项式不等式。该方法在理论上具备正确性、半完备性,且时间复杂度为次指数,空间复杂度为多项式,优于现有完全正确方法的双指数时间和至少指数空间。实验表明,在LRA和NRA基准测试中,该方法在求解实例数量和运行时间上均显著优于当前最先进的SMT求解器。

原文摘要 · Abstract (English)

The problem of checking satisfiability of linear real arithmetic (LRA) and non-linear real arithmetic (NRA) formulas has broad applications, in particular, they are at the heart of logic-related applications such as logic for artificial intelligence, program analysis, etc. While there has been much work on checking satisfiability of unquantified LRA and NRA formulas, the problem of checking satisfiability of quantified LRA and NRA formulas remains a significant challenge. The main bottleneck in the existing methods is a computationally expensive quantifier elimination step. In this work, we propose a novel method for efficient quantifier elimination in quantified LRA and NRA formulas. We propose a template-based Skolemization approach, where we automatically synthesize linear/polynomial Skolem functions in order to eliminate quantifiers in the formula. The key technical ingredients in our approach are Positivstellensätze theorems from algebraic geometry, which allow for an efficient manipulation of polynomial inequalities. Our method offers a range of appealing theoretical properties combined with a strong practical performance. On the theory side, our method is sound, semi-complete, and runs in subexponential time and polynomial space, as opposed to existing sound and complete quantifier elimination methods that run in doubly-exponential time and at least exponential space. On the practical side, our experiments show superior performance compared to state-of-the-art SMT solvers in terms of the number of solved instances and runtime, both on LRA and on NRA benchmarks.

逻辑推理量词消去SMT求解代数几何

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