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

T
Tailwind CSS Blog
P
Proofpoint News Feed
V
Visual Studio Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
爱范儿
爱范儿
Microsoft Azure Blog
Microsoft Azure Blog
Recent Announcements
Recent Announcements
Vercel News
Vercel News
Hugging Face - Blog
Hugging Face - Blog
GbyAI
GbyAI
博客园 - 聂微东
D
DataBreaches.Net
酷 壳 – CoolShell
酷 壳 – CoolShell
Microsoft Security Blog
Microsoft Security Blog
L
LangChain Blog
美团技术团队
H
Help Net Security
aimingoo的专栏
aimingoo的专栏
C
Check Point Blog
U
Unit 42
博客园 - 叶小钗
有赞技术团队
有赞技术团队
M
MIT News - Artificial intelligence
MongoDB | Blog
MongoDB | Blog

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
Towards the Locality of Vizing's Theorem
Hsin-Hao Su, Hoa T. Vu · 2019-01-03 · via cs.DS updates on arXiv.org

Vizing showed that it suffices to color the edges of a simple graph using $Δ+ 1$ colors, where $Δ$ is the maximum degree of the graph. However, up to this date, no efficient distributed edge-coloring algorithms are known for obtaining such a coloring, even for constant degree graphs. The current algorithms that get closest to this number of colors are the randomized $(Δ+ \tildeΘ(\sqrtΔ))$-edge-coloring algorithm that runs in $\text{polylog}(n)$ rounds by Chang et al. (SODA '18) and the deterministic $(Δ+ \text{polylog}(n))$-edge-coloring algorithm that runs in $\text{poly}(Δ, \log n)$ rounds by Ghaffari et al. (STOC '18). We present two distributed edge-coloring algorithms that run in $\text{poly}(Δ,\log n)$ rounds. The first algorithm, with randomization, uses only $Δ+2$ colors. The second algorithm is a deterministic algorithm that uses $Δ+ O(\log n/ \log \log n)$ colors. Our approach is to reduce the distributed edge-coloring problem into an online, restricted version of balls-into-bins problem. If $\ell$ is the maximum load of the bins, our algorithm uses $Δ+ 2\ell - 1$ colors. We show how to achieve $\ell = 1$ with randomization and $\ell = O(\log n / \log \log n)$ without randomization.