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

The GitHub Blog
The GitHub Blog
M
MIT News - Artificial intelligence
Engineering at Meta
Engineering at Meta
云风的 BLOG
云风的 BLOG
博客园 - 叶小钗
Jina AI
Jina AI
Last Week in AI
Last Week in AI
The Cloudflare Blog
博客园 - 【当耐特】
Stack Overflow Blog
Stack Overflow Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
WordPress大学
WordPress大学
博客园_首页
I
InfoQ
G
Google Developers Blog
Martin Fowler
Martin Fowler
Recent Announcements
Recent Announcements
H
Help Net Security
U
Unit 42
Blog — PlanetScale
Blog — PlanetScale
阮一峰的网络日志
阮一峰的网络日志
P
Proofpoint News Feed
IT之家
IT之家
Microsoft Security Blog
Microsoft Security 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
Routing in Unit Disk Graphs without Dynamic Headers
Wolfgang Mulzer, Max Willert · 2020-02-25 · via cs.DS updates on arXiv.org

Let $V\subset\mathbb{R}^2$ be a set of $n$ sites in the plane. The unit disk graph $DG(V)$ of $V$ is the graph with vertex set $V$ in which two sites $v$ and $w$ are adjacent if and only if their Euclidean distance is at most $1$. We develop a compact routing scheme for $DG(V)$. The routing scheme preprocesses $DG(V)$ by assigning a label $l(v)$ to every site $v$ in $V$. After that, for any two sites $s$ and $t$, the scheme must be able to route a packet from $s$ to $t$ as follows: given the label of a current vertex $r$ (initially, $r=s$) and the label of the target vertex $t$, the scheme determines a neighbor $r'$ of $r$. Then, the packet is forwarded to $r'$, and the process continues until the packet reaches its desired target $t$. The resulting path between the source $s$ and the target $t$ is called the routing path of $s$ and $t$. The stretch of the routing scheme is the maximum ratio of the total Euclidean length of the routing path and of the shortest path in $DG(V)$, between any two sites $s, t \in V$. We show that for any given $\varepsilon>0$, we can construct a routing scheme for $DG(V)$ with diameter $D$ that achieves stretch $1+\varepsilon$ and label size $O(\log D\log^3n/\log\log n)$ (the constant in the $O$-Notation depends on $\varepsilon$). In the past, several routing schemes for unit disk graphs have been proposed. Our scheme is the first one to achieve poly-logarithmic label size and arbitrarily small stretch without storing any additional information in the packet.