AIApr 26, 2024

Certified MaxSAT Preprocessing

arXiv:2404.17316v16 citationsh-index: 15IJCAR
Originality Incremental advance
AI Analysis

This work addresses the correctness concern for MaxSAT solvers, which is important for users relying on these tools for NP-hard optimization problems, though it is incremental as it builds on existing SAT proof logging methods.

The authors tackled the problem of ensuring correctness in MaxSAT solvers by extending proof logging to the preprocessing phase, demonstrating that their approach is feasible in practice through extensive evaluation on benchmarks.

Building on the progress in Boolean satisfiability (SAT) solving over the last decades, maximum satisfiability (MaxSAT) has become a viable approach for solving NP-hard optimization problems, but ensuring correctness of MaxSAT solvers has remained an important concern. For SAT, this is largely a solved problem thanks to the use of proof logging, meaning that solvers emit machine-verifiable proofs of (un)satisfiability to certify correctness. However, for MaxSAT, proof logging solvers have started being developed only very recently. Moreover, these nascent efforts have only targeted the core solving process, ignoring the preprocessing phase where input problem instances can be substantially reformulated before being passed on to the solver proper. In this work, we demonstrate how pseudo-Boolean proof logging can be used to certify the correctness of a wide range of modern MaxSAT preprocessing techniques. By combining and extending the VeriPB and CakePB tools, we provide formally verified, end-to-end proof checking that the input and preprocessed output MaxSAT problem instances have the same optimal value. An extensive evaluation on applied MaxSAT benchmarks shows that our approach is feasible in practice.

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