Optimization of Approximate Model Counting Method Based on Bivariate Decomposition

摘要：

　　This paper proposes an improved method for approximate model counting based on bivariate optimization.With the development of hash functions,scalable approximate model counting algorithms that can solve tens of thousands of variables in recent years have been proposed,improved and applied.However,in practical applications,there are still a large number of laws for the actual input data of different problems,and there is a lot of optimization space.This paper proposes an input variable optimization algorithm based on bivariate optimization for approximate model counting.It optimizes the structure of a large number of bivariate and univariate inputs in practical problems,and successfully improves the calculation time of the most approximate model counting problems without losing accuracy.

关键词： Approximate model counting bivariate decomposition #SAT

作者: Xuan Zhou Bin Zhang Yankun Tu Shuaiqi Wang Sihan Jia

作者单位: College of Computer Science and Technology,Jilin University,Changchun 130012,China

会议类型: 国际会议

会议名称: 2019 2nd International Conference on Mechanical Engineering, Industrial Materials and Industrial Electronics (MEIMIE 2019)2019年第二届机械工程、工业材料和工业电子国际会议(Meimie 2019)

会议地点: 大连

会议语种:英文

页码: 484-488

在线出版日期: 2019-03-29（万方平台首次上网日期，不代表论文的发表时间）