Lower bounds provide essential insights into the minimal computational resources required for algorithm execution. This paper focuses on logical theories, a domain where estimating resources is particularly difficult, and provides a novel, fully-automated method for computing lower bounds on memory usage, serving as a proxy for the computational resources required to perform logical reasoning.

Specifically, our method provides a lower bound on the size of the minimal deterministic finite automaton that encodes the solution set of a given Presburger arithmetic (also known as linear integer arithmetic) formula. The lower bounds are accompanied by independently verifiable certificates which also support a union-like operation that can be used to further increase the computed bounds.

We conducted an extensive empirical evaluation of our method using over 5000 formulae from the quantifier-free fragment of Presburger arithmetic, sourced from the SMT-LIB repository. The results show that our method often produces lower bounds that are close to the actual size of the minimal deterministic finite automaton. Moreover, it succeeds in computing non-trivial bounds even for instances that are out of reach (by several orders of magnitude) for the existing state-of-the-art automata-based tools for solving Presburger arithmetic.