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

WordPress大学
WordPress大学
Engineering at Meta
Engineering at Meta
D
DataBreaches.Net
月光博客
月光博客
Recent Announcements
Recent Announcements
Google DeepMind News
Google DeepMind News
U
Unit 42
腾讯CDC
爱范儿
爱范儿
J
Java Code Geeks
有赞技术团队
有赞技术团队
Blog — PlanetScale
Blog — PlanetScale
N
Netflix TechBlog - Medium
B
Blog
Stack Overflow Blog
Stack Overflow Blog
GbyAI
GbyAI
T
The Blog of Author Tim Ferriss
小众软件
小众软件
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Y
Y Combinator Blog
大猫的无限游戏
大猫的无限游戏
Microsoft Azure Blog
Microsoft Azure Blog
T
Tailwind CSS Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知

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
Nearly Optimal Bounds for Distributed Wireless Scheduling...
Magnus M. Halldorsson, Pradipta Mitra · 2011-04-28 · via cs.DS updates on arXiv.org

We study the wireless scheduling problem in the SINR model. More specifically, given a set of $n$ links, each a sender-receiver pair, we wish to partition (or \emph{schedule}) the links into the minimum number of slots, each satisfying interference constraints allowing simultaneous transmission. In the basic problem, all senders transmit with the same uniform power. We give a distributed $O(\log n)$-approximation algorithm for the scheduling problem, matching the best ratio known for centralized algorithms. It holds in arbitrary metric space and for every length-monotone and sublinear power assignment. It is based on an algorithm of Kesselheim and Vöcking, whose analysis we improve by a logarithmic factor. We show that every distributed algorithm uses $Ω(\log n)$ slots to schedule certain instances that require only two slots, which implies that the best possible absolute performance guarantee is logarithmic.