Improving the Lower Bound in Branch-and-Bound Algorithms for MaxSAT

The MaxSAT problem is an optimization version of the satisfiability problem (SAT). A tight lower bound (LB) on the number of falsified soft clauses in a MaxSAT solution is crucial for the efficiency of Branch-and-Bound (BnB) MaxSAT solvers. To compute an LB, modern BnB solvers detect disjoint inconsistent subsets of soft clauses, called cores, using unit propagation. A notable feature of these solvers is that soft clauses belonging to already detected cores cannot be reused to detect additional cores, limiting the number of cores that can be detected. In this paper, we propose an unlocking mechanism that allows the reuse of soft clauses in already detected cores while ensuring the soundness of LB. Experimental results show that this unlocking mechanism consistently improves the performance of a state-of-the-art BnB solver. In addition, it allowed us to win the first two places in the exact unweighted category of the MaxSAT Evaluation 2024.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i11.33226
Citations
1
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.89

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Improving the Lower Bound in Branch-and-Bound Algorithms for MaxSAT

Djamal Habet, Chu-Min Li, Felip Manyà, Jordi Coll et al.
1 citations
Proceedings of the AAAI Conference on Artificial Intelligence
Anomaly Detection Techniques and Applications
0.89
article

Improving the Lower Bound in Branch-and-Bound Algorithms for MaxSAT

Djamal Habet, Chu-Min Li, Felip Manyà, Jordi Coll, Shuolin Li
article en
1 citations

Abstract

The MaxSAT problem is an optimization version of the satisfiability problem (SAT). A tight lower bound (LB) on the number of falsified soft clauses in a MaxSAT solution is crucial for the efficiency of Branch-and-Bound (BnB) MaxSAT solvers. To compute an LB, modern BnB solvers detect disjoint inconsistent subsets of soft clauses, called cores, using unit propagation. A notable feature of these solvers is that soft clauses belonging to already detected cores cannot be reused to detect additional cores, limiting the number of cores that can be detected. In this paper, we propose an unlocking mechanism that allows the reuse of soft clauses in already detected cores while ensuring the soundness of LB. Experimental results show that this unlocking mechanism consistently improves the performance of a state-of-the-art BnB solver. In addition, it allowed us to win the first two places in the exact unweighted category of the MaxSAT Evaluation 2024.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(11)
National Academies of Sciences, Engineering, and Medicine (US), Aix-Marseille Université (FR), Universitat de Girona (ES), Artificial Intelligence Research Institute (ES), Université de Picardie Jules Verne (FR)
Aix-Marseille Université, Agence Nationale de la Recherche, Agencia Estatal de Investigación
Openalex Percentile: Top 28%
Anomaly Detection Techniques and Applications
0.89
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.