2026-07-24T03:16:07Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18747 2024-03-06T11:02:37Z ddc:004 open_access

Parameterized Matroid-Constrained Maximum Coverage Sellier, François Matroids approximate kernel maximum coverage 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik François Sellier 2023 Is Part Of LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ESA.2023.94 urn:nbn:de:0030-drops-187475 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.94 eng https://creativecommons.org/licenses/by/4.0/legalcode