SearcharxivSearch

arXiv · 2008.01590

List $k$-Colouring $P_t$-Free Graphs: a Mim-width Perspective

Abstract

A colouring of a graph $G=(V,E)$ is a mapping $c\colon V\to \{1,2,\ldots\}$ such that $c(u)\neq c(v)$ for every two adjacent vertices $u$ and $v$ of $G$. The {\sc List $k$-Colouring} problem is to decide whether a graph $G=(V,E)$ with a list $L(u)\subseteq \{1,\ldots,k\}$ for each $u\in V$ has a colouring $c$ such that $c(u)\in L(u)$ for every $u\in V$. Let $P_t$ be the path on $t$ vertices and let $K_{1,s}^1$ be the graph obtained from the $(s+1)$-vertex star $K_{1,s}$ by subdividing each of its edges exactly once.Recently, Chudnovsky, Spirkl and Zhong (DM 2020) proved that List $3$-Colouring is polynomial-time solvable for $(K_{1,s}^1,P_t)$-free graphs for every $t\geq 1$ and $s\geq 1$. We generalize their result to List $k$-Colouring for every $k\geq 1$. Our result also generalizes the known result that for every $k\geq 1$ and $s\geq 0$, List $k$-Colouring is polynomial-time solvable for $(sP_1+P_5)$-free graphs, which was proven for $s=0$ by Ho\`ang, Kami\'nski, Lozin, Sawada, and Shu (Algorithmica 2010) and for every $s\geq 1$ by Couturier, Golovach, Kratsch and Paulusma (Algorithmica 2015). We show our result by proving boundedness of an underlying width parameter. Namely, we show that for every $k\geq 1$, $s\geq 1$, $t\geq 1$, the class of $(K_k,K_{1,s}^1,P_t)$-free graphs has bounded mim-width and that a corresponding branch decomposition is "quickly computable" for these graphs.

Explore related subjects

Keep this discovery

BibTeXRIS

Nick Brettell, Jake Horsfield, Andrea Munaro, Daniel Paulusma. 2020-08-03. List $k$-Colouring $P_t$-Free Graphs: a Mim-width Perspective. https://doi.org/10.1016/j.ipl.2021.106168

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS