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Structure-Guided Local Improvement for Maximum Satisfiability

Authors André Schidler , Stefan Szeider

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Author Details

André Schidler
  • Algorithms and Complexity Group, TU Wien, Austria
Stefan Szeider
  • Algorithms and Complexity Group, TU Wien, Austria

Funding

Austrian Science Fund (FWF), project 10.55776/P36420.

Acknowledgements

Part of this work was carried out while taking part in the Dagstuhl Seminar 23261 "SAT Encodings and Beyond," as well as in the extended reunion of the program "Satisfiability: Theory, Practice, and Beyond" in the spring of 2023 at the Simons Institute for the Theory of Computing at UC Berkeley.

Cite AsGet BibTex

André Schidler and Stefan Szeider. Structure-Guided Local Improvement for Maximum Satisfiability. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 26:1-26:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.26

Abstract

The enhanced performance of today’s MaxSAT solvers has elevated their appeal for many large-scale applications, notably in software analysis and computer-aided design. Our research delves into refining anytime MaxSAT solving by repeatedly identifying and solving with an exact solver smaller subinstances that are chosen based on the graphical structure of the instance. We investigate various strategies to pinpoint these subinstances. This structure-guided selection of subinstances provides an exact solver with a high potential for improving the current solution. Our exhaustive experimental analyses contrast our methodology as instantiated in our tool MaxSLIM with previous studies and benchmark it against leading-edge MaxSAT solvers.

Subject Classification

ACM Subject Classification
  • Hardware → Theorem proving and SAT solving
  • Theory of computation → Discrete optimization
  • Theory of computation → Automated reasoning
  • Theory of computation → Constraint and logic programming
  • General and reference → Experimentation
Keywords
  • maximum satisfiability
  • large neighborhood search (LNS)
  • SAT-based local improvement (SLIM)
  • incomplete MaxSAT
  • graphical structure
  • metaheuristic

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References

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