License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2023.94
URN: urn:nbn:de:0030-drops-187475
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Sellier, François

Parameterized Matroid-Constrained Maximum Coverage

LIPIcs-ESA-2023-94.pdf (0.7 MB)


In this paper, we introduce the concept of Density-Balanced Subset in a matroid, in which independent sets can be sampled so as to guarantee that (i) each element has the same probability to be sampled, and (ii) those events are negatively correlated. These Density-Balanced Subsets are subsets in the ground set of a matroid in which the traditional notion of uniform random sampling can be extended.
We then provide an application of this concept to the Matroid-Constrained Maximum Coverage problem. In this problem, given a matroid ℳ = (V, ℐ) of rank k on a ground set V and a coverage function f on V, the goal is to find an independent set S ∈ ℐ maximizing f(S). This problem is an important special case of the much-studied submodular function maximization problem subject to a matroid constraint; this is also a generalization of the maximum k-cover problem in a graph. In this paper, assuming that the coverage function has a bounded frequency μ (i.e., any element of the underlying universe of the coverage function appears in at most μ sets), we design a procedure, parameterized by some integer ρ, to extract in polynomial time an approximate kernel of size ρ ⋅ k that is guaranteed to contain a 1 - (μ - 1)/ρ approximation of the optimal solution. This procedure can then be used to get a Fixed-Parameter Tractable Approximation Scheme (FPT-AS) providing a 1 - ε approximation in time (μ/ε)^O(k) ⋅ |V|^O(1). This generalizes and improves the results of [Manurangsi, 2019] and [Huang and Sellier, 2022], providing the first FPT-AS working on an arbitrary matroid. Moreover, as the kernel has a very simple characterization, it can be constructed in the streaming setting.

BibTeX - Entry

  author =	{Sellier, Fran\c{c}ois},
  title =	{{Parameterized Matroid-Constrained Maximum Coverage}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{94:1--94:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-187475},
  doi =		{10.4230/LIPIcs.ESA.2023.94},
  annote =	{Keywords: Matroids, approximate kernel, maximum coverage}

Keywords: Matroids, approximate kernel, maximum coverage
Collection: 31st Annual European Symposium on Algorithms (ESA 2023)
Issue Date: 2023
Date of publication: 30.08.2023

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