2026-07-24T18:11:16Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:13654 2024-03-06T10:52:34Z ddc:004 open_access

Tight Approximation Guarantees for Concave Coverage Problems Barman, Siddharth Fawzi, Omar Fermé, Paul Approximation Algorithms Coverage Problems Concave Function In the maximum coverage problem, we are given subsets T_1, …, T_m of a universe [n] along with an integer k and the objective is to find a subset S ⊆ [m] of size k that maximizes C(S) : = |⋃_{i ∈ S} T_i|. It is a classic result that the greedy algorithm for this problem achieves an optimal approximation ratio of 1-e^{-1}. In this work we consider a generalization of this problem wherein an element a can contribute by an amount that depends on the number of times it is covered. Given a concave, nondecreasing function φ, we define C^{φ}(S) := ∑_{a ∈ [n]}w_aφ(|S|_a), where |S|_a = |{i ∈ S : a ∈ T_i}|. The standard maximum coverage problem corresponds to taking φ(j) = min{j,1}. For any such φ, we provide an efficient algorithm that achieves an approximation ratio equal to the Poisson concavity ratio of φ, defined by α_{φ} : = min_{x ∈ ℕ^*} 𝔼[φ(Poi(x))] / φ(𝔼[Poi(x)]). Complementing this approximation guarantee, we establish a matching NP-hardness result when φ grows in a sublinear way. As special cases, we improve the result of [Siddharth Barman et al., 2020] about maximum multi-coverage, that was based on the unique games conjecture, and we recover the result of [Szymon Dudycz et al., 2020] on multi-winner approval-based voting for geometrically dominant rules. Our result goes beyond these special cases and we illustrate it with applications to distributed resource allocation problems, welfare maximization problems and approval-based voting for general rules. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Siddharth Barman and Omar Fawzi and Paul Fermé 2021 Is Part Of LIPIcs, Volume 187, 38th International Symposium on Theoretical Aspects of Computer Science (STACS 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.STACS.2021.9 urn:nbn:de:0030-drops-136543 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2021.9 eng https://creativecommons.org/licenses/by/4.0/legalcode