Work

Research on Abstraction-Based Search Space Partitioning and Solving Satisfiability Problems

Year: 2025

Type: article

Abstract: Solving satisfiability problems is central to many areas of computer science, including artificial intelligence and optimization. Efficiently solving satisfiability problems requires exploring vast search spaces, where search space partitioning plays a key role in improving solving efficiency. This paper defines search spaces and their partitioning, focusing on the relationship between partitioning strategies and satisfiability problem solving. By introducing an abstraction method for partitioning the search space—distinct from traditional assignment-based approaches—the paper proposes sequential, parallel, and hybrid solving algorithms. Experimental results show that the hybrid approach, combining abstraction and assignment, significantly accelerates solving in most cases. Furthermore, a unified method for search space partitioning is presented, defining independent and complete partitions. This method offers a new direction for enhancing the efficiency of SAT problem solving and provides a foundation for future research in the field.

Source: Mathematics

DOI: 10.3390/math13050868

Authors: Yuexin Huang, Qinzhou Niu, Yanfang Song

Institution: Guilin University of Technology

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China