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Documents authored by Yin, Minghao

Document
DOI: 10.4230/LIPIcs.CP.2023.41
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, and Minghao Yin

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

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.
Cite as

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)

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@InProceedings{zhou_et_al:LIPIcs.CP.2023.41,
  author =	{Zhou, Wenbo and Zhao, Yujiao and Wang, Yiyuan and Cai, Shaowei and Wang, Shimao and Wang, Xinyu and Yin, Minghao},
  title =	{{Improving Local Search for Pseudo Boolean Optimization by Fragile Scoring Function and Deep Optimization}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{41:1--41:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.41},
  URN =		{urn:nbn:de:0030-drops-190784},
  doi =		{10.4230/LIPIcs.CP.2023.41},
  annote =	{Keywords: Local Search, Pseudo-Boolean Optimization, Deep Optimization}
}
Document
DOI: 10.4230/LIPIcs.SAT.2023.29
LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem

Authors: Junping Zhou, Jiaxin Liang, Minghao Yin, and Bo He

Published in: LIPIcs, Volume 271, 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)

Abstract
The Maximum Satisfiability (MaxSAT), an important optimization problem, has a range of applications, including network routing, planning and scheduling, and combinatorial auctions. Among these applications, one usually benefits from having not just one single solution, but k diverse solutions. Motivated by this, we study an extension of MaxSAT, named Diversified Top-k MaxSAT (DTKMS) problem, which is to find k feasible assignments of a given formula such that each assignment satisfies all hard clauses and all of them together satisfy the maximum number of soft clauses. This paper presents a local search algorithm, LS-DTKMS, for DTKMS problem, which exploits novel scoring functions to select variables and assignments. Experiments demonstrate that LS-DTKMS outperforms the top-k MaxSAT based DTKMS solvers and state-of-the-art solvers for diversified top-k clique problem.
Cite as

Junping Zhou, Jiaxin Liang, Minghao Yin, and Bo He. LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem. In 26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 271, pp. 29:1-29:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{zhou_et_al:LIPIcs.SAT.2023.29,
  author =	{Zhou, Junping and Liang, Jiaxin and Yin, Minghao and He, Bo},
  title =	{{LS-DTKMS: A Local Search Algorithm for Diversified Top-k MaxSAT Problem}},
  booktitle =	{26th International Conference on Theory and Applications of Satisfiability Testing (SAT 2023)},
  pages =	{29:1--29:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-286-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{271},
  editor =	{Mahajan, Meena and Slivovsky, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.29},
  URN =		{urn:nbn:de:0030-drops-184912},
  doi =		{10.4230/LIPIcs.SAT.2023.29},
  annote =	{Keywords: Top-k, MaxSAT, local search}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.32
A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems

Authors: Hongbo Li, Yaling Wu, Minghao Yin, and Zhanshan Li

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Variable ordering heuristics (VOH) play a central role in solving Constraint Satisfaction Problems (CSP). The performance of different VOHs may vary greatly in solving the same CSP instance. In this paper, we propose an approach to select efficient VOHs for solving different CSP instances. The approach contains two phases. The first phase is a probing procedure that runs a simple portfolio strategy containing several different VOHs. The portfolio tries to use each of the candidate VOHs to guide backtracking search to solve the CSP instance within a limited number of failures. If the CSP is not solved by the portfolio, one of the candidates is selected for it by analysing the information attached in the search trees generated by the candidates. The second phase uses the selected VOH to guide backtracking search to solve the CSP. The experiments are run with the MiniZinc benchmark suite and four different VOHs which are considered as the state of the art are involved. The results show that the proposed approach finds the best VOH for more than 67% instances and it solves more instances than all the candidate VOHs and an adaptive VOH based on Multi-Armed Bandit. It could be an effective adaptive search strategy for black-box CSP solvers.
Cite as

Hongbo Li, Yaling Wu, Minghao Yin, and Zhanshan Li. A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 32:1-32:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{li_et_al:LIPIcs.CP.2022.32,
  author =	{Li, Hongbo and Wu, Yaling and Yin, Minghao and Li, Zhanshan},
  title =	{{A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{32:1--32:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.32},
  URN =		{urn:nbn:de:0030-drops-166616},
  doi =		{10.4230/LIPIcs.CP.2022.32},
  annote =	{Keywords: Constraint Satisfaction Problem, Variable Ordering Heuristic, Adaptive Search Heuristic, Portfolio}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2021.9
Failure Based Variable Ordering Heuristics for Solving CSPs (Short Paper)

Authors: Hongbo Li, Minghao Yin, and Zhanshan Li

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Variable ordering heuristics play a central role in solving constraint satisfaction problems. In this paper, we propose failure based variable ordering heuristics. Following the fail first principle, the new heuristics use two aspects of failure information collected during search. The failure rate heuristics consider the failure proportion after the propagations of assignments of variables and the failure length heuristics consider the length of failures, which is the number of fixed variables composing a failure. We performed a vast experiments in 41 problems with 1876 MiniZinc instances. The results show that the failure based heuristics outperform the existing ones including activity-based search, conflict history search, the refined weighted degree and correlation-based search. They can be new candidates of general purpose variable ordering heuristics for black-box CSP solvers.
Cite as

Hongbo Li, Minghao Yin, and Zhanshan Li. Failure Based Variable Ordering Heuristics for Solving CSPs (Short Paper). In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 9:1-9:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{li_et_al:LIPIcs.CP.2021.9,
  author =	{Li, Hongbo and Yin, Minghao and Li, Zhanshan},
  title =	{{Failure Based Variable Ordering Heuristics for Solving CSPs}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{9:1--9:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.9},
  URN =		{urn:nbn:de:0030-drops-153002},
  doi =		{10.4230/LIPIcs.CP.2021.9},
  annote =	{Keywords: Constraint Satisfaction Problem, Variable Ordering Heuristic, Failure Rate, Failure Length}
}
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