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Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization

Authors Wenbo Zhou , Yujiao Zhao , Yiyuan Wang , Shaowei Cai , Shimao Wang, Xinyu Wang, Minghao Yin

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ACM Subject Classification
  • Computing methodologies → Search methodologies
Keywords
  • Local Search
  • Pseudo-Boolean Optimization
  • Deep Optimization

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Abstract

Pseudo-Boolean optimization (PBO) is usually used to model combinatorial optimization problems, especially for some real-world applications. Despite its significant importance in both theory and applications, there are few works on using local search to solve PBO. This paper develops a novel local search framework for PBO, which has three main ideas. First, we design a two-level selection strategy to evaluate all candidate variables. Second, we propose a novel deep optimization strategy to disturb some search spaces. Third, a sampling flipping method is applied to help the algorithm jump out of local optimum. Experimental results show that the proposed algorithms outperform three state-of-the-art PBO algorithms on most instances.

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Wenbo Zhou, Yujiao Zhao, Yiyuan Wang, Shaowei Cai, Shimao Wang, Xinyu Wang, and Minghao Yin. Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 41:1-41:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.CP.2023.41

Author Details

Wenbo Zhou
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
  • Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China
  • Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE, Jilin University, Changchun, China
Yujiao Zhao
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
Yiyuan Wang
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
  • Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China
Shaowei Cai
  • State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China
  • School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
Shimao Wang
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
Xinyu Wang
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
Minghao Yin
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
  • Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China

Funding

This work is supported by the National Natural Science Foundation of China under Grant No.61806050, the Natural Science Research Foundation of Jilin Province of China under Grant Nos.YDZJ202201ZYTS412 and YDZJ202201ZYTS423, Ministry of Education under Grant No.2021BCF01002, CCF-Huawei Populus Grove Fund, and the Fundamental Research Funds for the Central Universities under Grant Nos.2412023YQ003, 2412022ZD015, 2412022ZD018, 2412022QD040 and 93K172022K10.

Supplementary Materials

References

  1. Carlos Ansotegui, Fahiem Bacchus, Matti Järvisalo, and Ruben Martins. MaxSAT evaluation 2017: Solver and benchmark descriptions, 2017. Google Scholar
  2. Jeremias Berg, Emilia Oikarinen, Matti Järvisalo, and Kai Puolamäki. Minimum-width confidence bands via constraint optimization. In Proceedings of the Twenty-Third Principles and Practice of Constraint Programming, volume 10416, pages 443-459, 2017. Google Scholar
  3. Daniel Le Berre and Romain Wallon. On dedicated CDCL strategies for PB solvers. In Proceedings of the Twenty-Fourth Theory and Applications of Satisfiability Testing, volume 12831, pages 315-331, 2021. Google Scholar
  4. Ksenia Bestuzheva, Mathieu Besançon, Wei-Kun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Oliver Gaul, Gerald Gamrath, Ambros Gleixner, et al. The SCIP optimization suite 8.0. arXiv:2112.08872, 2021. Google Scholar
  5. Frédéric Boussemart, Fred Hemery, Christophe Lecoutre, and Lakhdar Sais. Boosting systematic search by weighting constraints. In Proceedings of the Sixteenth Eureopean Conference on Artificial Intelligence, volume 16, pages 146-150, 2004. Google Scholar
  6. Shaowei Cai, Jinkun Lin, and Chuan Luo. Finding a small vertex cover in massive sparse graphs: Construct, local search, and preprocess. Journal of Artificial Intelligence Research, 59:463-494, 2017. Google Scholar
  7. Shaowei Cai and Kaile Su. Comprehensive score: Towards efficient local search for SAT with long clauses. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pages 489-495, 2013. Google Scholar
  8. Jiejiang Chen, Shaowei Cai, Yiyuan Wang, Wenhao Xu, Jia Ji, and Minghao Yin. Improved local search for the minimum weight dominating set problem in massive graphs by using a deep optimization mechanism. Artificial Intelligence, 314:103819, 2023. Google Scholar
  9. Jo Devriendt, Ambros Gleixner, and Jakob Nordström. Learn to relax: Integrating 0-1 integer linear programming with pseudo-Boolean conflict-driven search. Constraints, 26(1):26-55, 2021. Google Scholar
  10. Jo Devriendt, Stephan Gocht, Emir Demirović, Jakob Nordström, and Peter J. Stuckey. Cutting to the core of pseudo-Boolean optimization: Combining core-guided search with cutting planes reasoning. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, volume 35, pages 3750-3758, 2021. Google Scholar
  11. Elizabeth D. Dolan and Jorge J. Moré. Benchmarking optimization software with performance profiles. Mathematical Programming, 91:201-213, 2002. Google Scholar
  12. Jan Elffers and Jakob Nordström. Divide and conquer: Towards faster pseudo-Boolean solving. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pages 1291-1299, 2018. Google Scholar
  13. Jan Elffers and Jakob Nordström. A cardinal improvement to pseudo-Boolean solving. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, volume 34, pages 1495-1503, 2020. Google Scholar
  14. John Franco and John Martin. A history of satisfiability. In Handbook of Satisfiability, volume 185 of Frontiers in Artificial Intelligence and Applications, pages 3-74. 2009. Google Scholar
  15. Pascal Van Hentenryck and Laurent Michel. Constraint-Based Local Search. The MIT press, 2009. Google Scholar
  16. Gergely Kovásznai, Balázs Erdélyi, and Csaba Biró. Investigations of graph properties in terms of wireless sensor network optimization. In Proceedings of the IEEE International Conference on Future IoT Technologies, pages 1-8, 2018. Google Scholar
  17. Gergely Kovásznai, Krisztián Gajdár, and Laura Kovács. Portfolio SAT and SMT solving of cardinality constraints in sensor network optimization. In Proceedings of the Twenty-First International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, pages 85-91, 2019. Google Scholar
  18. Zhendong Lei, Shaowei Cai, Chuan Luo, and Holger H. Hoos. Efficient local search for pseudo Boolean optimization. In Proceedings of the Twenty-Fourth Theory and Applications of Satisfiability Testing, volume 12831, pages 332-348, 2021. Google Scholar
  19. Zhendong Lei, Yiyuan Wang, Shiwei Pan, Shaowei Cai, and Minghao Yin. CASHWMaxSAT-CorePlus: Solver description. MaxSAT Evaluation, page 8, 2022. Google Scholar
  20. Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Leslie Pérez Cáceres, Mauro Birattari, and Thomas Stützle. The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives, 3:43-58, 2016. Google Scholar
  21. Ruben Martins, Vasco Manquinho, and Inês Lynce. Open-WBO: A modular maxsat solver. In Proceedings of the Seventeenth Theory and Applications of Satisfiability Testing, volume 8561, pages 438-445, 2014. Google Scholar
  22. David Pisinger. Where are the hard knapsack problems? Computers & Operations Research, 32(9):2271-2284, 2005. Google Scholar
  23. Olivier Roussel and Vasco Manquinho. Pseudo-Boolean and cardinality constraints. In Handbook of Satisfiability, volume 336 of Frontiers in Artificial Intelligence and Applications, pages 1087-1129. 2021. Google Scholar
  24. Masahiko Sakai and Hidetomo Nabeshima. Construction of an ROBDD for a PB-constraint in band form and related techniques for PB-solvers. IEICE Transactions on Information and Systems, E98.D(6):1121-1127, 2015. Google Scholar
  25. Paul Shaw. Using constraint programming and local search methods to solve vehicle routing problems. In Proceedings of the Fourth Principles and Practice of Constraint Programming, volume 1520, pages 417-431, 1998. Google Scholar
  26. Pavel Smirnov, Jeremias Berg, and Matti Järvisalo. Pseudo-Boolean optimization by implicit hitting sets. In Proceedings of the Twenty-Seventh Principles and Practice of Constraint Programming, volume 210, pages 51:1-51:20, 2021. Google Scholar
  27. Pavel Smirnov, Jeremias Berg, and Matti Järvisalo. Improvements to the implicit hitting set approach to pseudo-Boolean optimization. In Proceedings of the Twenty-Fifth International Conference on Theory and Applications of Satisfiability Testing, volume 236, pages 13:1-13:18, 2022. Google Scholar
  28. Zhouxing Su, Qingyun Zhang, Zhipeng Lü, Chu-Min Li, Weibo Lin, and Fuda Ma. Weighting-based variable neighborhood search for optimal camera placement. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, volume 35, pages 12400-12408, 2021. Google Scholar
  29. Renato Tinós, Michal W. Przewozniczek, and Darrell Whitley. Iterated local search with perturbation based on variables interaction for pseudo-Boolean optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 296-304, 2022. Google Scholar
  30. Marc Vinyals, Jan Elffers, Jesús Giráldez-Cru, Stephan Gocht, and Jakob Nordström. In between resolution and cutting planes: A study of proof systems for pseudo-Boolean SAT solving. In Proceedings of the Twenty-First Theory and Applications of Satisfiability Testing, volume 10929, pages 292-310, 2018. Google Scholar
  31. Chris Voudouris and Edward Tsang. Partial constraint satisfaction problems and guided local search. In Proceedings of the Practical Application of Constraint Technology, pages 337-356, 1996. Google Scholar
  32. Yiyuan Wang, Shaowei Cai, Jiejiang Chen, and Minghao Yin. Sccwalk: An efficient local search algorithm and its improvements for maximum weight clique problem. Artificial Intelligence, 280:103230, 2020. Google Scholar
  33. Yiyuan Wang, Dantong Ouyang, Liming Zhang, and Minghao Yin. A novel local search for unicost set covering problem using hyperedge configuration checking and weight diversity. Science China Information Sciences, 60:062103, 2017. Google Scholar
  34. Jiongzhi Zheng, Jianrong Zhou, and Kun He. Farsighted probabilistic sampling based local search for (weighted) partial maxsat. CoRR, abs/2108.09988, 2021. Google Scholar
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