Towards More Efficient Local Search for Pseudo-Boolean Optimization

Authors Yi Chu , Shaowei Cai , Chuan Luo , Zhendong Lei , Cong Peng

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Author Details

Yi Chu
  • Institute of Software, Chinese Academy of Sciences, Beijing, China
Shaowei Cai
  • State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
Chuan Luo
  • School of Software, Beihang University, Beijing, China
Zhendong Lei
  • State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
Cong Peng
  • Finovation in CCBFT, Beijing, China

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Yi Chu, Shaowei Cai, Chuan Luo, Zhendong Lei, and Cong Peng. Towards More Efficient Local Search for Pseudo-Boolean Optimization. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 12:1-12:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Pseudo-Boolean (PB) constraints are highly expressive, and many combinatorial optimization problems can be modeled using pseudo-Boolean optimization (PBO). It is recognized that stochastic local search (SLS) is a powerful paradigm for solving combinatorial optimization problems, but the development of SLS for solving PBO is still in its infancy. In this paper, we develop an effective SLS algorithm for solving PBO, dubbed NuPBO, which introduces a novel scoring function for PB constraints and a new weighting scheme. We conduct experiments on a broad range of six public benchmarks, including three real-world benchmarks, a benchmark from PB competition, an integer linear programming optimization benchmark, and a crafted combinatorial benchmark, to compare NuPBO against five state-of-the-art competitors, including a recently-proposed SLS PBO solver LS-PBO, two complete PB solvers PBO-IHS and RoundingSat, and two mixed integer programming (MIP) solvers Gurobi and SCIP. NuPBO has been exhibited to perform best on these three real-world benchmarks. On the other three benchmarks, NuPBO shows competitive performance compared to state-of-the-art competitors, and it significantly outperforms LS-PBO, indicating that NuPBO greatly advances the state of the art in SLS for solving PBO.

Subject Classification

ACM Subject Classification
  • Theory of computation → Randomized local search
  • Pseudo-Boolean Optimization
  • Stochastic Local Search
  • Scoring Function
  • Weighting Scheme


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