# Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers

arXiv: https://arxiv.org/abs/2108.09988 · HTML: https://mlsanity.com/paper/2108.09988/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Learning Features and Abstract Actions for Computing Generalized Plans](https://mlsanity.com/paper/1811.07231/) (2018)

## Built on by (most cited first, 4 of 4)

- [Rethinking the Soft Conflict Pseudo Boolean Constraint on MaxSAT Local Search Solvers](https://mlsanity.com/paper/2401.10589/) (2024)
- [Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis](https://mlsanity.com/paper/2403.06568/) (2024)
- [Cohesive Group Discovery in Interaction Graphs under Explicit Density Constraints](https://mlsanity.com/paper/2508.04174/) (2025)
- [Enhancing Local Search for MaxSAT with Deep Differentiation Clause Weighting](https://mlsanity.com/paper/2512.05619/) (2025)

## Abstract (arXiv)

Local search has been demonstrated as an efficient approach for two practical generalizations of the MaxSAT problem, namely Partial MaxSAT (PMS) and Weighted PMS (WPMS). In this work, we observe that most local search (W)PMS solvers usually flip a single variable per iteration. Such a mechanism may lead to relatively low-quality local optimal solutions, and may limit the diversity of search directions to escape from local optima. To address this issue, we propose a general strategy, called farsighted probabilistic sampling (FPS), to replace the single flipping mechanism so as to boost the local search (W)PMS algorithms. FPS considers the benefit of continuously flipping a pair of variables in order to find higher-quality local optimal solutions. Moreover, FPS proposes an effective approach to escape from local optima by preferring the best to flip among the best sampled single variable and the best sampled variable pair. Extensive experiments demonstrate that our proposed FPS strategy significantly improves the state-of-the-art (W)PMS solvers, and FPS has an excellent generalization capability to various local search MaxSAT solvers.