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.
@InProceedings{jabs_et_al:LIPIcs.CP.2023.18, author = {Jabs, Christoph and Berg, Jeremias and Ihalainen, Hannes and J\"{a}rvisalo, Matti}, title = {{Preprocessing in SAT-Based Multi-Objective Combinatorial Optimization}}, booktitle = {29th International Conference on Principles and Practice of Constraint Programming (CP 2023)}, pages = {18:1--18:20}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-300-3}, ISSN = {1868-8969}, year = {2023}, volume = {280}, editor = {Yap, Roland H. C.}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.18}, URN = {urn:nbn:de:0030-drops-190553}, doi = {10.4230/LIPIcs.CP.2023.18}, annote = {Keywords: maximum satisfiability, multi-objective combinatorial optimization, preprocessing, redundancy} }
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