Bounding Fixed Points of Non-Monotone Processes: Theory to Practice

TL;DR

This paper develops practical methods for approximating fixed points of non-monotone processes, improving efficiency and precision for applications like answer set programming and speculative analysis.

Contribution

It introduces a sound, efficient algorithm with controlled unsoundness for tightening approximations of non-monotone operators, making Approximation Fixpoint Theory practical for programming languages.

Findings

Algorithm guarantees termination on finite-height lattices.

Modified algorithm ensures polynomial-time complexity.

Applied in answer set programming and speculative analysis with practical benefits.

Abstract

Many modern solvers and program analyzers rely on non-monotone reasoning (e.g. negation-as-failure, speculative updates, backtracking) for which classical monotone fixed-point methods do not apply. The general problem of finding the fixed points of these processes is a difficult one. For this reason, there have been theoretical efforts in existing Approximation Fixpoint Theory (AFT) from the domain of logic programming to approximate fixed points of non-monotone operators. Tight approximations of these fixed points are highly useful for accelerating non-monotonic computations by restricting the search space. In practice, however, even the best approximations obtained through AFT can be coarse and computationally expensive. We aim to address both issues to make AFT approximation methods practical for use in programming languages (PL) settings. To mitigate inefficiency, we prove the soundness of an abstract interpretation for approximating operators. To improve upon coarse approximations, we carefully introduce controlled unsoundness to design an effective yet practical algorithm for partitioning and tightening AFT's best approximations. This algorithm is sound, anytime, and guarantees termination on finite-height lattices. We further present a modification that ensures polynomial-time complexity. We instantiate these methods in two settings: (1) answer set programming, where it serves as a convergence-accelerating pre-processor, and (2) speculative program analysis, where it reduces rollback while preserving soundness. In both settings, we focus on implementation-level details to demonstrate the practical applicability of our methods.