We consider the Sparse Hitting Set (Sparse-HS) problem, where we are given a set system $(V,\mathcal{F},\mathcal{B})$ with two families $\mathcal{F},\mathcal{B}$ of subsets of $V$. The task is to find a hitting set for $\mathcal{F}$ that minimizes the maximum number of elements in any of the sets of $\mathcal{B}$. Our focus is on determining the complexity of some special cases of Sparse-HS with respect to the sparseness $k$, which is the optimum number of hitting set elements in any set of $\mathcal{B}$. For the Sparse Vertex Cover (Sparse-VC) problem, $V$ is given by the vertex set of a graph, and $\mathcal{F}$ is its edge set. We prove NP-hardness for sparseness $k\geq 2$ and polynomial time solvability for $k=1$. We also provide a polynomial-time $2$-approximation for any $k$. A special case of Sparse-VC is Fair Vertex Cover (Fair-VC), where the family $\mathcal{B}$ is given by vertex neighbourhoods. For this problem we prove NP-hardness for constant $k$ and provide a polynomial-time $(2-\frac{1}{k})$-approximation. This is better than any approximation possible for Sparse-VC or Vertex Cover (under UGC). We then consider two problems derived from Sparse-HS related to the highway dimension, a graph parameter modelling transportation networks. Most algorithms for graphs of low highway dimension compute solutions to the $r$-Shortest Path Cover ($r$-SPC) problem, where $r>0$, $\mathcal{F}$ contains all shortest paths of length between $r$ and $2r$, and $\mathcal{B}$ contains all balls of radius $2r$. There is an XP algorithm that computes solutions to $r$-SPC of sparseness at most $h$ if the input graph has highway dimension $h$, but the existence if an FPT algorithm was open. We prove that $r$-SPC and also the related $r$-Highway Dimension ($r$-HD) problem are both W[1]-hard. Furthermore, we prove that $r$-SPC admits a polynomial-time $O(\log n)$-approximation.
翻译:我们考虑Sprassy Hitting Set (Sparse-HS) 问题。 我们的焦点是确定一个固定系统$( V,\ mathcal{F},\ mathcal{B} 美元) 的美元, 有两个家庭$( mathcal{F},\ mathcal{B} 美元) 的子集美元。 任务在于找到一个为$( mathcal{F} 美元) 的顶点, 将每套美元( mathcal{B} 美元) 的最多元素数量最小。 我们的焦点是确定Spress- husfr$( Sprick- 美元) 的特点的复杂性。 美元- 美元( 美元) 美元( 美元) 的顶点, 美元( 美元) 美元( 美元) 美元( 美元) 的Splickr=1 的内存最佳数目。 对于美元( 美元) 的解点, 我们的解点( 美元) 或美元( 美元) 美元) 的解解解( 美元) 的内解是两个。