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Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization

Authors Christoph Jabs , Jeremias Berg , Hannes Ihalainen , Matti Järvisalo

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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatorial optimization
  • Theory of computation → Constraint and logic programming
Keywords
  • maximum satisfiability
  • multi-objective combinatorial optimization
  • preprocessing
  • redundancy

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Abstract

Building on Boolean satisfiability (SAT) and maximum satisfiability (MaxSAT) solving algorithms, several approaches to computing Pareto-optimal MaxSAT solutions under multiple objectives have been recently proposed. However, preprocessing in (Max)SAT-based multi-objective optimization remains so-far unexplored. Generalizing clause redundancy to the multi-objective setting, we establish provably-correct liftings of MaxSAT preprocessing techniques for multi-objective MaxSAT in terms of computing Pareto-optimal solutions. We also establish preservation of Pareto-MCSes - the multi-objective lifting of minimal correction sets tightly connected to optimal MaxSAT solutions - as a distinguishing feature between different redundancy notions in the multi-objective setting. Furthermore, we provide a first empirical evaluation of the effect of preprocessing on instance sizes and multi-objective MaxSAT solvers.

Cite As Get BibTex

Christoph Jabs, Jeremias Berg, Hannes Ihalainen, and Matti Järvisalo. Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 18:1-18:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.CP.2023.18

Author Details

Christoph Jabs
  • HIIT, Department of Computer Science, University of Helsinki, Finland
Jeremias Berg
  • HIIT, Department of Computer Science, University of Helsinki, Finland
Hannes Ihalainen
  • HIIT, Department of Computer Science, University of Helsinki, Finland
Matti Järvisalo
  • HIIT, Department of Computer Science, University of Helsinki, Finland

Funding

Work financially supported by Academy of Finland under grants 322869, 342145 and 356046.

Acknowledgements

The authors wish to thank the Finnish Computing Competence Infrastructure (FCCI) for supporting this project with computational and data storage resources.

Supplementary Materials

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