Leroy Chew

Sravanthi Chede, Leroy Chew, Vaibhav Krishan et al.

For a quantified Boolean formula (QBF), the problem of computing the number of winning strategies is known as the #QBF problem. This problem is considered harder than the analogous #SAT problem. Recently, important proof systems for QBFs and #SAT have been studied. By extending the ideas from both fields, we show that it is possible to design proof systems for #QBF. Such proof systems are important not only for advancing the theory of #QBF but also for certifying and designing better #QBF solvers, an area that is still in its early stages. In this paper, we explore #QBF proof systems to count the number of Skolem functions. Apart from a naive system, we study #QBF systems based on the expansion rule of universal variables in QBFs. We observe that these systems have inherent structural weaknesses that lead to lower bounds. As an alternative, we propose a #QBF proof system that we call Q-MICE, which consists of sound inference rules for computing and certifying the #QBF solution, similar to the line-based #SAT proof system MICE. To demonstrate the strength of Q-MICE, we present various upper bounds, such as the quantified version of the propositional XOR-PAIRS formula, which are known to be hard for MICE. Consequently, we also separate Q-MICE from the expansion-based #QBF proof systems.

Leroy Chew, Tomáš Peitl

Certification for Quantified Boolean Formulas (QBF) and Dependency Quantified Boolean Formulas (DQBF) is an ongoing challenge. Recent proof complexity work has shown that the majority of QBF and DQBF techniques can be p-simulated by using the independent extension rule. In propositional logic, extension rules are supported by proof checkers using a more general RAT (Resolution Asymmetric Tautology) rule. The next step in (D)QBF certification would be to update these modern RAT formats to match the strength of this independent extension rule. In this paper we first introduce a new dependency scheme called Dpure. This rule is the missing ingredient that when added to Blinkhorn's proof system DQRAT allows it to be provably p-equivalent to the Independent Extended QU-Res, the most powerful of the known QBF and DQBF proof systems. Up until now, DQRAT has only existed in theory, so we implement a prototype checker DQRAT-check which includes our extra rule. In addition to its inclusion in our proof checker we show Dpure has other properties that have been found for previous dependency schemes, and each of these observations has potential in solving/checking including the sound integration into the dependency learning solver Qute.

Michael Hartisch, Leroy Chew

Quantified Integer Programming (QIP) bridges multiple domains by extending Quantified Boolean Formulas (QBF) to incorporate general integer variables and linear constraints while also generalizing Integer Programming through variable quantification. As a special case of Quantified Constraint Satisfaction Problems (QCSP), QIP provides a versatile framework for addressing complex decision-making scenarios. Additionally, the inclusion of a linear objective function enables QIP to effectively model multistage robust discrete linear optimization problems, making it a powerful tool for tackling uncertainty in optimization. While two primary solution paradigms exist for QBF -- search-based and expansion-based approaches -- only search-based methods have been explored for QIP and QCSP. We introduce an expansion-based approach for QIP using Counterexample-Guided Abstraction Refinement (CEGAR), adapting techniques from QBF. We extend this methodology to tackle multistage robust discrete optimization problems with linear constraints and further embed it in an optimization framework, enhancing its applicability. Our experimental results highlight the advantages of this approach, demonstrating superior performance over existing search-based solvers for QIP in specific instances. Furthermore, the ability to model problems using linear constraints enables notable performance gains over state-of-the-art expansion-based solvers for QBF.