Mycielski graphs are a family of triangle-free graphs 𝑀_𝑘 with arbitrarily high chromatic number. 𝑀_𝑘 has chromatic number k and there is a short informal proof of this fact, yet finding proofs of it via automated reasoning techniques has proved to be a challenging task. In this paper, we study the complexity of clausal proofs of the uncolorability of 𝑀_𝑘 with 𝑘−1 colors. In particular, we consider variants of the PR (propagation redundancy) proof system that are without new variables, and with or without deletion. These proof systems are of interest due to their potential uses for proof search. As our main result, we present a sublinear-length and constant-width PR proof without new variables or deletion. We also implement a proof generator and verify the correctness of our proof. Furthermore, we consider formulas extended with clauses from the proof until a short resolution proof exists, and investigate the performance of CDCL in finding the short proof. This turns out to be difficult for CDCL with the standard heuristics. Finally, we describe an approach inspired by SAT sweeping to find proofs of these extended formulas.
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Preprocessing of Propagation Redundant Clauses
The propagation redundant (PR) proof system generalizes the resolution and resolution asymmetric tautology proof systems used by conflict-driven clause learning (CDCL) solvers. PR allows short proofs of unsatisfiability for some problems that are difficult for CDCL solvers. Previous attempts to automate PR clause learning used hand-crafted heuristics that work well on some highly-structured problems. For example, the solver SaDiCaL incorporates PR clause learning into the CDCL loop, but it cannot compete with modern CDCL solvers due to its fragile heuristics.
We present prExtract, a preprocessing technique that learns short PR clauses. Adding these clauses to a formula reduces the search space that the solver must explore. By performing PR clause learning as a preprocessing stage, PR clauses can be found efficiently without sacrificing the robustness of modern CDCL solvers. On a large portion of SAT competition benchmarks we found that preprocessing with prExtract improves solver performance. In addition, there were several satisfiable and unsatisfiable formulas that could only be solved after preprocessing with prExtract. prExtract supports proof logging, giving a high level of confidence in the results.
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- Award ID(s):
- 2108521
- NSF-PAR ID:
- 10410365
- Editor(s):
- Blanchette, Jasmin; Kovacs, Laura; Pattinson, Dirk
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Lecture notes in computer science
- Edition / Version:
- Lecture Notes in Computer Science
- Volume:
- 12651
- Issue:
- 1
- ISSN:
- 0302-9743
- Page Range / eLocation ID:
- 108-124
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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