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Documents authored by Lin, Peng

Document
DOI: 10.4230/LIPIcs.CP.2025.26
Parallel MIP Solving with Dynamic Task Decomposition

Authors: Peng Lin, Shaowei Cai, Mengchuan Zou, and Shengqi Chen

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Mixed Integer Programming (MIP) is a foundational model in operations research. Although significant progress has been made in enhancing sequential MIP solvers through sophisticated techniques and heuristics, remarkable developments in computing resources have made parallel solving a promising direction for performance improvement. In this work, we propose a novel parallel MIP solving framework that employs dynamic task decomposition in a divide-and-conquer paradigm. Our framework incorporates a hardness estimate heuristic to identify challenging solving tasks and a reward decaying mechanism to reinforce the task decomposition decision. We apply our framework to two state-of-the-art open-source MIP solvers, SCIP and HiGHS, yielding efficient parallel solvers. Extensive experiments on the full MIPLIB benchmark, using up to 128 cores, demonstrate that our framework yields substantial performance improvements over modern divide-and-conquer parallel solvers. Moreover, our parallel solvers have established new best known solutions for 16 open MIPLIB instances.
Cite as

Peng Lin, Shaowei Cai, Mengchuan Zou, and Shengqi Chen. Parallel MIP Solving with Dynamic Task Decomposition. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 26:1-26:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lin_et_al:LIPIcs.CP.2025.26,
  author =	{Lin, Peng and Cai, Shaowei and Zou, Mengchuan and Chen, Shengqi},
  title =	{{Parallel MIP Solving with Dynamic Task Decomposition}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{26:1--26:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.26},
  URN =		{urn:nbn:de:0030-drops-238871},
  doi =		{10.4230/LIPIcs.CP.2025.26},
  annote =	{Keywords: Mixed Integer Programming, Parallel Computing, Complete Search, Task Decomposition}
}
Artifact
Software
DOI: 10.4230/artifacts.22500
Local-MIP

Authors: Peng Lin, Mengchuan Zou, and Shaowei Cai

Abstract
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Peng Lin, Mengchuan Zou, Shaowei Cai. Local-MIP (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@misc{dagstuhl-artifact-22500,
   title = {{Local-MIP}}, 
   author = {Lin, Peng and Zou, Mengchuan and Cai, Shaowei},
   note = {Software, version 1.0., swhId: \href{https://archive.softwareheritage.org/swh:1:dir:883191ffb9b4503105cce3e9d3da6d50421956f3;origin=https://github.com/shaowei-cai-group/Local-MIP;visit=swh:1:snp:34d027907a0a0b75fa84bfa7f8345ee2ee858337;anchor=swh:1:rev:09f5626d23710b0a1a0b7b38ad910f5b25ae7096}{\texttt{swh:1:dir:883191ffb9b4503105cce3e9d3da6d50421956f3}} (visited on 2024-11-28)},
   url = {https://github.com/shaowei-cai-group/Local-MIP},
   doi = {10.4230/artifacts.22500},
}
Document
DOI: 10.4230/LIPIcs.CP.2024.5
ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization

Authors: Zhihan Chen, Peng Lin, Hao Hu, and Shaowei Cai

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
As a broadly applied technique in numerous optimization problems, recently, local search has been employed to solve Pseudo-Boolean Optimization (PBO) problem. A representative local search solver for PBO is LS-PBO. In this paper, firstly, we improve LS-PBO by a dynamic scoring mechanism, which dynamically strikes a balance between score on hard constraints and score on the objective function. Moreover, on top of this improved LS-PBO, we develop the first parallel local search PBO solver. The main idea is to share good solutions among different threads to guide the search, by maintaining a pool of feasible solutions. For evaluating solutions when updating the pool, we propose a function that considers both the solution quality and the diversity of the pool. Furthermore, we calculate the polarity density in the pool to enhance the scoring function of local search. Our empirical experiments show clear benefits of the proposed parallel approach, making it competitive with the parallel version of the famous commercial solver Gurobi.
Cite as

Zhihan Chen, Peng Lin, Hao Hu, and Shaowei Cai. ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 5:1-5:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{chen_et_al:LIPIcs.CP.2024.5,
  author =	{Chen, Zhihan and Lin, Peng and Hu, Hao and Cai, Shaowei},
  title =	{{ParLS-PBO: A Parallel Local Search Solver for Pseudo Boolean Optimization}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{5:1--5:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.5},
  URN =		{urn:nbn:de:0030-drops-206900},
  doi =		{10.4230/LIPIcs.CP.2024.5},
  annote =	{Keywords: Pseudo-Boolean Optimization, Parallel Solving, Local Search, Scoring Function, Solution Pool}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.19
An Efficient Local Search Solver for Mixed Integer Programming

Authors: Peng Lin, Mengchuan Zou, and Shaowei Cai

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Mixed integer programming (MIP) is a fundamental model in operations research. Local search is a powerful method for solving hard problems, but the development of local search solvers for MIP still needs to be explored. This work develops an efficient local search solver for solving MIP, called Local-MIP. We propose two new operators for MIP to adaptively modify variables for optimizing the objective function and satisfying constraints, respectively. Furthermore, we design a new weighting scheme to dynamically balance the priority between the objective function and each constraint, and propose a two-level scoring function structure to hierarchically guide the search for high-quality feasible solutions. Experiments are conducted on seven public benchmarks to compare Local-MIP with state-of-the-art MIP solvers, which demonstrate that Local-MIP significantly outperforms CPLEX, HiGHS, SCIP and Feasibility Jump, and is competitive with the most powerful commercial solver Gurobi. Moreover, Local-MIP establishes 4 new records for MIPLIB open instances.
Cite as

Peng Lin, Mengchuan Zou, and Shaowei Cai. An Efficient Local Search Solver for Mixed Integer Programming. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 19:1-19:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{lin_et_al:LIPIcs.CP.2024.19,
  author =	{Lin, Peng and Zou, Mengchuan and Cai, Shaowei},
  title =	{{An Efficient Local Search Solver for Mixed Integer Programming}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{19:1--19:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.19},
  URN =		{urn:nbn:de:0030-drops-207041},
  doi =		{10.4230/LIPIcs.CP.2024.19},
  annote =	{Keywords: Mixed Integer Programming, Local Search, Operator, Scoring Function}
}
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