arXiv:2501.15156 · v3

Quantifier Elimination and Craig Interpolation, Quantitatively

首次提交 2025-01-25 17:38

版本更新 2026-11-09 00:17

雷达观测 2026-11-10 12:16

摘要

Quantifier elimination (QE) and Craig interpolation (CI) are central to various state-of-the-art automated approaches to hardware- and software verification. They are rooted in the Boolean setting and are successful for, e.g., first-order theories such as linear rational arithmetic. What about their applicability in the quantitative setting where formulae evaluate to numbers and quantitative supremum/infimum quantifiers are the natural counterpart to Boolean quantifiers? Applications include establishing quantitative properties of programs such as bounds on expected outcomes of probabilistic programs featuring non-determinism and analyzing the flow of information through programs. In this paper, we present, to the best of our knowledge, the first QE algorithm for possibly unbounded, $\infty$- or $(-\infty)$-valued, or discontinuous piecewise linear quantities. They are the quantitative counterpart to linear rational arithmetic, and they are a popular quantitative assertion language for probabilistic program verification. We provide rigorous soundness proofs as well as upper bounds on the size of the output. Moreover, we present two applications of our QE algorithm. First, our algorithm yields a quantitative CI theorem: Given arbitrary piecewise linear quantities $f,g$ with $f \models g$ (i.e., $f$ is at most $g$ at every variable valuation), both the strongest and the weakest Craig interpolants are quantifier-free and effectively constructible. Such interpolants lie between the two quantities and use only their common free variables. Second, we apply our QE algorithm to computing minimal and maximal expected outcomes of loop-free probabilistic programs featuring unbounded non-determinism, and to checking inductivity of candidate upper bounds on minimal and maximal expected outcomes of probabilistic loops featuring unbounded non-determinism.