Parameterized (Approximate) Defective Coloring

TL;DR

This paper explores the complexity and approximability of defective coloring in graphs, revealing fixed-parameter tractability for feedback vertex set and establishing ETH-based lower bounds for treewidth and pathwidth.

Contribution

It provides new fixed-parameter algorithms for defective coloring parameterized by feedback vertex set and proves hardness results for other parameters, along with approximation algorithms and bounds.

Findings

W-hardness for treewidth, pathwidth, tree-depth, feedback vertex set when χ_d=2

FPT algorithm for feedback vertex set for any χ_d ≥ 2

ETH-based lower bounds for treewidth and pathwidth

Abstract

In Defective Coloring we are given a graph $G = (V, E)$ and two integers $\chi_d, \Delta^*$ and are asked if we can partition $V$ into $\chi_d$ color classes, so that each class induces a graph of maximum degree $\Delta^*$. We investigate the complexity of this generalization of Coloring with respect to several well-studied graph parameters, and show that the problem is W-hard parameterized by treewidth, pathwidth, tree-depth, or feedback vertex set, if $\chi_d = 2$. As expected, this hardness can be extended to larger values of $\chi_d$ for most of these parameters, with one surprising exception: we show that the problem is FPT parameterized by feedback vertex set for any $\chi_d \ge 2$, and hence 2-coloring is the only hard case for this parameter. In addition to the above, we give an ETH-based lower bound for treewidth and pathwidth, showing that no algorithm can solve the problem in $n^{o(pw)}$, essentially matching the complexity of an algorithm obtained with standard techniques. We complement these results by considering the problem's approximability and show that, with respect to $\Delta^*$, the problem admits an algorithm which for any $\epsilon > 0$ runs in time $(tw/\epsilon)^{O(tw)}$ and returns a solution with exactly the desired number of colors that approximates the optimal $\Delta^*$ within $(1 + \epsilon)$. We also give a $(tw)^{O(tw)}$ algorithm which achieves the desired $\Delta^*$ exactly while 2-approximating the minimum value of $\chi_d$. We show that this is close to optimal, by establishing that no FPT algorithm can (under standard assumptions) achieve a better than $3/2$-approximation to $\chi_d$, even when an extra constant additive error is also allowed.