Tight Approximation Guarantees for Concave Coverage Problems
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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 [Barman et al., IPCO, 2020] about maximum multi-coverage, that was based on the unique games conjecture, and we recover the result of [Dudycz et al., IJCAI, 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.
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