Quantified Linear and Polynomial Arithmetic Satisfiability via Template-based Skolemization

TL;DR

This paper introduces a novel, efficient template-based Skolemization method for quantifier elimination in linear and polynomial real arithmetic formulas, significantly improving theoretical complexity and practical performance over existing approaches.

Contribution

The authors propose a new Skolemization technique using algebraic geometry to eliminate quantifiers efficiently in LRA and NRA formulas, with strong theoretical and practical advantages.

Findings

Method is sound and semi-complete

Runs in subexponential time and polynomial space

Outperforms state-of-the-art SMT solvers in experiments

Abstract

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 Positivstellens\"atze 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.