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

D
DataBreaches.Net
L
LangChain Blog
博客园_首页
J
Java Code Geeks
博客园 - 【当耐特】
Microsoft Azure Blog
Microsoft Azure Blog
小众软件
小众软件
WordPress大学
WordPress大学
V
Visual Studio Blog
T
The Blog of Author Tim Ferriss
U
Unit 42
酷 壳 – CoolShell
酷 壳 – CoolShell
Recent Announcements
Recent Announcements
C
Check Point Blog
IT之家
IT之家
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
A
About on SuperTechFans
aimingoo的专栏
aimingoo的专栏
D
Docker
有赞技术团队
有赞技术团队
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
阮一峰的网络日志
阮一峰的网络日志
I
InfoQ

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
A Reconfigurations Analogue of Brooks' Theorem and its Co...
Carl Feghali, Matthew Johnson, Daniël Paulusma · 2015-01-23 · via cs.DS updates on arXiv.org

Let $G$ be a simple undirected graph on $n$ vertices with maximum degree~$Δ$. Brooks' Theorem states that $G$ has a $Δ$-colouring unless~$G$ is a complete graph, or a cycle with an odd number of vertices. To recolour $G$ is to obtain a new proper colouring by changing the colour of one vertex. We show an analogue of Brooks' Theorem by proving that from any $k$-colouring, $k>Δ$, a $Δ$-colouring of $G$ can be obtained by a sequence of $O(n^2)$ recolourings using only the original $k$ colours unless $G$ is a complete graph or a cycle with an odd number of vertices, or $k=Δ+1$, $G$ is $Δ$-regular and, for each vertex $v$ in $G$, no two neighbours of $v$ are coloured alike. We use this result to study the reconfiguration graph $R_k(G)$ of the $k$-colourings of $G$. The vertex set of $R_k(G)$ is the set of all possible $k$-colourings of $G$ and two colourings are adjacent if they differ on exactly one vertex. We prove that for $Δ\geq 3$, $R_{Δ+1}(G)$ consists of isolated vertices and at most one further component which has diameter $O(n^2)$. This result enables us to complete both a structural classification and an algorithmic classification for reconfigurations of colourings of graphs of bounded maximum degree.