Work

Integer Linear Programming Preprocessing for Maximum Satisfiability

Year: 2025

Type: conference-paper

Abstract: The Maximum Satisfiability problem (MaxSAT) is a major optimization challenge with numerous practical applications. In recent MaxSAT evaluations, most MaxSAT solvers have incorporated an Integer Linear Programming (ILP) solver into their portfolios. However, a good portfolio strategy requires a lot of tuning work and is limited to the profiling benchmark. This paper proposes a methodology to fully integrate ILP preprocessing techniques into the MaxSAT solving pipeline and investigates the impact on the top-performing MaxSAT solvers. Experimental results show that our approach helps to improve 5 out of 6 state-of-the-art MaxSAT solvers, especially for WMaxCDCLOpenWbo1200, the winner of the MaxSAT evaluation 2024 on the unweighted track, which is able to solve 15 additional instances using our methodology.

Source: IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

DOI: 10.1109/ictai66417.2025.00061

Authors: Jialu Zhang, Chu-Min Li, Sami Cherif, Shuolin Li, Zhifei Zheng

Institutions: Modélisation, information et systèmes, Université de Picardie Jules Verne, Centre National de la Recherche Scientifique, Aix-Marseille Université, Laboratoire d’Informatique et Systèmes

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Agence Nationale de la Recherche, Conseil Régional Hauts-de-France, European Regional Development Fund

Award: Branching for Satisfiability and Beyond