2 Search Results for "Lentz, Antonin"

Document
DOI: 10.4230/LIPIcs.SEA.2025.27
Sparsity-Driven Aggregation of Mixed Integer Programs

Authors: Liding Xu, Gioni Mexi, and Ksenia Bestuzheva

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
Cutting planes are crucial for the performance of branch-and-cut algorithms for solving mixed-integer programming (MIP) problems, and linear row aggregation has been successfully applied to better leverage the potential of several major families of MIP cutting planes. This paper formulates the problem of finding good quality aggregations as an 𝓁₀-norm minimization problem and employs a combination of the lasso method and iterative reweighting to efficiently find sparse solutions corresponding to good aggregations. A comparative analysis of the proposed algorithm and the state-of-the-art greedy heuristic approach is presented, showing that the greedy heuristic implements a stepwise selection algorithm for the 𝓁₀-norm minimization problem. Further, we present an example where our approach succeeds, whereas the standard heuristic fails to find an aggregation with desired properties. The algorithm is implemented within the constraint integer programming solver SCIP, and computational experiments on the MIPLIB 2017 benchmark show that although the algorithm leads to slowdowns on relatively "easier" instances, our aggregation approach decreases the mean running time on a subset of challenging instances and leads to smaller branch-and-bound trees.
Cite as

Liding Xu, Gioni Mexi, and Ksenia Bestuzheva. Sparsity-Driven Aggregation of Mixed Integer Programs. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 27:1-27:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{xu_et_al:LIPIcs.SEA.2025.27,
  author =	{Xu, Liding and Mexi, Gioni and Bestuzheva, Ksenia},
  title =	{{Sparsity-Driven Aggregation of Mixed Integer Programs}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{27:1--27:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.27},
  URN =		{urn:nbn:de:0030-drops-232652},
  doi =		{10.4230/LIPIcs.SEA.2025.27},
  annote =	{Keywords: mixed integer linear programming, cutting plane, valid inequality, separation, aggregation, projection, sparse optimization}
}
Document
DOI: 10.4230/OASIcs.ATMOS.2020.11
Framing Algorithms for Approximate Multicriteria Shortest Paths

Authors: Nicolas Hanusse, David Ilcinkas, and Antonin Lentz

Published in: OASIcs, Volume 85, 20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020)

Abstract
This paper deals with the computation of d-dimensional multicriteria shortest paths. In a weighted graph with arc weights represented by vectors, the cost of a path is the vector sum of the weights of its arcs. For a given pair consisting of a source s and a destination t, a path P dominates a path Q if and only if P’s cost is component-wise smaller than or equal to Q’s cost. The set of Pareto paths, or Pareto set, from s to t is the set of paths that are not dominated. The computation time of the Pareto paths can be prohibitive whenever the set of Pareto paths is large. We propose in this article new algorithms to compute approximated Pareto paths in any dimension. For d = 2, we exhibit the first approximation algorithm, called Frame, whose output is guaranteed to be always a subset of the Pareto set. Finally, we provide a small experimental study in order to confirm the relevance of our Frame algorithm.
Cite as

Nicolas Hanusse, David Ilcinkas, and Antonin Lentz. Framing Algorithms for Approximate Multicriteria Shortest Paths. In 20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020). Open Access Series in Informatics (OASIcs), Volume 85, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{hanusse_et_al:OASIcs.ATMOS.2020.11,
  author =	{Hanusse, Nicolas and Ilcinkas, David and Lentz, Antonin},
  title =	{{Framing Algorithms for Approximate Multicriteria Shortest Paths}},
  booktitle =	{20th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2020)},
  pages =	{11:1--11:19},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-170-2},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{85},
  editor =	{Huisman, Dennis and Zaroliagis, Christos D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2020.11},
  URN =		{urn:nbn:de:0030-drops-131476},
  doi =		{10.4230/OASIcs.ATMOS.2020.11},
  annote =	{Keywords: Pareto set, multicriteria, shortest paths, approximation}
}
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