BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit
Not on the map. Why?
Abstract (arXiv)
We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMaxSAT, that applies a multi-armed bandit model to guide the search direction. The bandit in our method is associated with all the soft clauses in the input (W)PMS instance. Each arm corresponds to a soft clause. The bandit model can help BandMaxSAT to select a good direction to escape from local optima by selecting a soft clause to be satisfied in the current step, that is, selecting an arm to be pulled. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate that BandMaxSAT significantly outperforms the state-of-the-art (W)PMS local search algorithm SATLike3.0. Specifically, the number of instances in which BandMaxSAT obtains better results is about twice that obtained by SATLike3.0. Moreover, we combine BandMaxSAT with the complete solver TT-Open-WBO-Inc. The resulting solver BandMaxSAT-c also outperforms some of the best state-of-the-art complete (W)PMS solvers, including SATLike-c, Loandra and TT-Open-WBO-Inc.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 5 of its 39 references that are in the map. First posted on arXiv 14 Jan 2022.
Builds on
5 of 39 references.
References · 5
- 2019Introduction to Multi-Armed Banditscited by 475
- 2018Learning Features and Abstract Actions for Computing Generalized Planscited by 35
- 2019Randomized Allocation with Nonparametric Estimation for Contextual Multi-Armed Bandits with Delayed Rewardscited by 15
- 2021Incomplete MaxSAT Approaches for Combinatorial Testingcited by 5
- 2019Cascaded Algorithm-Selection and Hyper-Parameter Optimization with Extreme-Region Upper Confidence Bound Banditcited by 2
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 2; 2026: 1.
6 papers.
- 2024Application of AI to formal methods - an analysis of current trendscited by 2
- 2022Incorporating Multi-armed Bandit with Local Search for MaxSATcited by 0
- 2024Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysiscited by 0
- 2025Integer Linear Programming Preprocessing for Maximum Satisfiabilitycited by 0
- 2025Enhancing Local Search for MaxSAT with Deep Differentiation Clause Weightingcited by 0
- 2026Offline Local Search for Online Stochastic Banditscited by 0
- 2026Offline Local Search for Online Stochastic Bandits10 Apr 2026
- 2025Enhancing Local Search for MaxSAT with Deep Differentiation Clause Weighting5 Dec 2025
- 2025Integer Linear Programming Preprocessing for Maximum Satisfiability6 Jun 2025
- 2024Application of AI to formal methods - an analysis of current trends22 Nov 2024
- 2024Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis11 Mar 2024
- 2022Incorporating Multi-armed Bandit with Local Search for MaxSAT29 Nov 2022