惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - Franky
Microsoft Azure Blog
Microsoft Azure Blog
阮一峰的网络日志
阮一峰的网络日志
宝玉的分享
宝玉的分享
量子位
N
Netflix TechBlog - Medium
M
MIT News - Artificial intelligence
GbyAI
GbyAI
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
博客园 - 叶小钗
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
酷 壳 – CoolShell
酷 壳 – CoolShell
T
Tailwind CSS Blog
Y
Y Combinator Blog
L
LangChain Blog
The Cloudflare Blog
T
The Blog of Author Tim Ferriss
U
Unit 42
Martin Fowler
Martin Fowler
aimingoo的专栏
aimingoo的专栏
G
Google Developers Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
List $k$-Colouring $P_t$-Free Graphs: a Mim-width Perspec...
Nick Brettell, Jake Horsfield, Andrea Munaro, Daniel Paulusma · 2020-08-03 · via cs.DS updates on arXiv.org

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 Hoàng, Kamiński, 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.