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Documents authored by Zou, Mengchuan

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Software
DOI: 10.4230/artifacts.22500
Local-MIP

Authors: Peng Lin, Mengchuan Zou, and Shaowei Cai

Abstract
Cite as

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.2024.19
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}
}
Document
DOI: 10.4230/LIPIcs.ICALP.2017.84
Approximation Strategies for Generalized Binary Search in Weighted Trees

Authors: Dariusz Dereniowski, Adrian Kosowski, Przemyslaw Uznanski, and Mengchuan Zou

Published in: LIPIcs, Volume 80, 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)

Abstract
We consider the following generalization of the binary search problem. A search strategy is required to locate an unknown target node t in a given tree T. Upon querying a node v of the tree, the strategy receives as a reply an indication of the connected component of T\{v} containing the target t. The cost of querying each node is given by a known non-negative weight function, and the considered objective is to minimize the total query cost for a worst-case choice of the target. Designing an optimal strategy for a weighted tree search instance is known to be strongly NP-hard, in contrast to the unweighted variant of the problem which can be solved optimally in linear time. Here, we show that weighted tree search admits a quasi-polynomial time approximation scheme (QPTAS): for any 0 < epsilon < 1, there exists a (1+epsilon)-approximation strategy with a computation time of n^O(log n / epsilon^2). Thus, the problem is not APX-hard, unless NP is contained in DTIME(n^O(log n)). By applying a generic reduction, we obtain as a corollary that the studied problem admits a polynomial-time O(sqrt(log n))-approximation. This improves previous tilde-O(log n)-approximation approaches, where the tilde-O-notation disregards O(poly log log n)-factors.
Cite as

Dariusz Dereniowski, Adrian Kosowski, Przemyslaw Uznanski, and Mengchuan Zou. Approximation Strategies for Generalized Binary Search in Weighted Trees. In 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 80, pp. 84:1-84:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{dereniowski_et_al:LIPIcs.ICALP.2017.84,
  author =	{Dereniowski, Dariusz and Kosowski, Adrian and Uznanski, Przemyslaw and Zou, Mengchuan},
  title =	{{Approximation Strategies for Generalized Binary Search in Weighted Trees}},
  booktitle =	{44th International Colloquium on Automata, Languages, and Programming (ICALP 2017)},
  pages =	{84:1--84:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-041-5},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{80},
  editor =	{Chatzigiannakis, Ioannis and Indyk, Piotr and Kuhn, Fabian and Muscholl, Anca},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2017.84},
  URN =		{urn:nbn:de:0030-drops-74507},
  doi =		{10.4230/LIPIcs.ICALP.2017.84},
  annote =	{Keywords: Approximation Algorithm, Adaptive Algorithm, Graph Search, Binary Search, Vertex Ranking, Trees}
}
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