Conditionally Tight Algorithms for Maximum k-Coverage and Partial k-Dominating Set via Arity-Reducing Hypercuts

TL;DR

This work studies the precise, fine-grained running times for Maximum $k$-Coverage and Partial $k$-Dominating Set, focusing on parameterizations that reflect problem structure rather than input size alone. The authors introduce arity-reducing hypercuts and bundles to develop a matching algorithm that, in many regimes, either matches or is conditionally optimal with respect to strong hypotheses such as the $k$-clique, 3-uniform hyperclique, and $k$-OV conjectures. They derive time bounds for Partial $k$-Dominating Set that depend on $t$ (the optimum) and, for Max $k$-Cover, depend on $n,u,s,f$, plus a regularization step to handle small-universe cases; they also extend results to sparse graphs with tight lower bounds. The central technical innovation—arity-reducing hypercuts—provides a unified framework that yields both faster algorithms and robust conditional lower bounds, bridging algorithm design and computational hardness in fine-grained complexity. Overall, the paper advances the understanding of when and how near-optimal exponential-time barriers can be broken for fundamental coverage problems, with potential implications for network design and combinatorial optimization in practice.

Abstract

We revisit the classic Maximum $k$-Coverage problem: Determine the largest number $t$ of elements that can be covered by choosing $k$ sets from a given family $\mathcal{F} = \{S_1,\dots, S_n\}$ of a size-$u$ universe. A notable special case is Partial $k$-Dominating Set, where one chooses $k$ vertices in a graph to maximize the number of dominated vertices. Extensive research has established strong hardness results for various aspects of Maximum $k$-Coverage, such as tight inapproximability results, $W[2]$-hardness, and a conditionally tight worst-case running time of $n^{k\pm o(1)}$. In this paper we ask: (1) Can this time bound be improved for small $t$, at least for Partial $k$-Dominating Set, ideally to time~$t^{k\pm O(1)}$? (2) More ambitiously, can we even determine the best-possible running time of Maximum $k$-Coverage with respect to the perhaps most natural parameters: the universe size $u$, the maximum set size $s$, and the maximum frequency $f$? We successfully resolve both questions. (1) We give an algorithm that solves Partial $k$-Dominating Set in time $O(nt + t^{\frac{2ω}{3} k+O(1)})$ if $ω\ge 2.25$ and time $O(nt+ t^{\frac{3}{2} k+O(1)})$ if $ω\le 2.25$, where $ω\le 2.372$ is the matrix multiplication exponent. From this we derive a time bound that is conditionally optimal, regardless of $ω$, based on the well-established $k$-clique and 3-uniform hyperclique hypotheses from fine-grained complexity. We also obtain matching upper and lower bounds for sparse graphs. To address (2) we design an algorithm for Maximum $k$-Coverage running in time $$ \min \left\{ (f\cdot \min\{\sqrt[3]{u}, \sqrt{s}\})^k + \min\{n,f\cdot \min\{\sqrt{u}, s\}\}^{kω/3}, n^k\right\} \cdot g(k)n^{\pm O(1)}, $$ and, surprisingly, further show that this complicated time bound is also conditionally optimal.

Theorems & Definitions (98)

• theorem 1.1: Fine-grained Complexity of Partial $k$-Dominating Set, informal version
• theorem 1.2: Fine-grained Complexity of Max $k$-Cover, informal version
• theorem 1.3: Partial $k$-Dominating Set in sparse graphs
• proposition 1.4
• theorem 1.5
• proof
• theorem 1.6
• theorem 1.7
• theorem 1.8
• Definition 1.9