Lossy kernels for connected distance-$r$ domination on nowhere dense graph classes

TL;DR

This paper demonstrates the existence of lossy kernels for connected distance-$r$ domination problems on nowhere dense graph classes, providing approximation guarantees and size bounds, while also proving limitations on more general classes.

Contribution

It establishes lossy kernelization results for connected distance-$r$ domination on nowhere dense graphs and shows such results do not extend to some broader classes.

Findings

Existence of polynomial-sized $eta$-approximate bi-kernels for nowhere dense classes.

Limitations of lossy kernelization on somewhere dense graph classes.

Dependence of kernel size on parameters and approximation factor.

Abstract

For $\alpha\colon\mathbb{N}\rightarrow\mathbb{R}$, an $\alpha$-approximate bi-kernel is a polynomial-time algorithm that takes as input an instance $(I, k)$ of a problem $Q$ and outputs an instance $(I',k')$ of a problem $Q'$ of size bounded by a function of $k$ such that, for every $c\geq 1$, a $c$-approximate solution for the new instance can be turned into a $c\cdot\alpha(k)$-approximate solution of the original instance in polynomial time. This framework of \emph{lossy kernelization} was recently introduced by Lokshtanov et al. We prove that for every nowhere dense class of graphs, every $\alpha>1$ and $r\in\mathbb{N}$ there exists a polynomial $p$ (whose degree depends only on $r$ while its coefficients depend on $\alpha$) such that the connected distance-$r$ dominating set problem with parameter $k$ admits an $\alpha$-approximate bi-kernel of size $p(k)$. Furthermore, we show that this result cannot be extended to more general classes of graphs which are closed under taking subgraphs by showing that if a class $C$ is somewhere dense and closed under taking subgraphs, then for some value of $r\in\mathbb{N}$ there cannot exist an $\alpha$-approximate bi-kernel for the (connected) distance-$r$ dominating set problem on $C$ for any function $\alpha\colon\mathbb{N}\rightarrow\mathbb{R}$ (assuming the Gap Exponential Time Hypothesis).