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

月光博客
月光博客
IT之家
IT之家
Hugging Face - Blog
Hugging Face - Blog
J
Java Code Geeks
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
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
博客园 - 叶小钗
MyScale Blog
MyScale Blog
G
Google Developers Blog
Microsoft Azure Blog
Microsoft Azure Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
大猫的无限游戏
大猫的无限游戏
博客园 - 三生石上(FineUI控件)
Google DeepMind News
Google DeepMind News
Engineering at Meta
Engineering at Meta
The Cloudflare Blog
Martin Fowler
Martin Fowler
酷 壳 – CoolShell
酷 壳 – CoolShell
N
Netflix TechBlog - Medium
MongoDB | Blog
MongoDB | Blog
I
InfoQ
WordPress大学
WordPress大学
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
H
Help Net Security

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
Online Euclidean Spanners
Sujoy Bhore, Csaba D. Tóth · 2021-07-02 · via cs.DS updates on arXiv.org

In this paper, we study the online Euclidean spanners problem for points in $\mathbb{R}^d$. Suppose we are given a sequence of $n$ points $(s_1,s_2,\ldots, s_n)$ in $\mathbb{R}^d$, where point $s_i$ is presented in step~$i$ for $i=1,\ldots, n$. The objective of an online algorithm is to maintain a geometric $t$-spanner on $S_i=\{s_1,\ldots, s_i\}$ for each step~$i$. First, we establish a lower bound of $Ω(\varepsilon^{-1}\log n / \log \varepsilon^{-1})$ for the competitive ratio of any online $(1+\varepsilon)$-spanner algorithm, for a sequence of $n$ points in 1-dimension. We show that this bound is tight, and there is an online algorithm that can maintain a $(1+\varepsilon)$-spanner with competitive ratio $O(\varepsilon^{-1}\log n / \log \varepsilon^{-1})$. Next, we design online algorithms for sequences of points in $\mathbb{R}^d$, for any constant $d\ge 2$, under the $L_2$ norm. We show that previously known incremental algorithms achieve a competitive ratio $O(\varepsilon^{-(d+1)}\log n)$. However, if the algorithm is allowed to use additional points (Steiner points), then it is possible to substantially improve the competitive ratio in terms of $\varepsilon$. We describe an online Steiner $(1+\varepsilon)$-spanner algorithm with competitive ratio $O(\varepsilon^{(1-d)/2} \log n)$. As a counterpart, we show that the dependence on $n$ cannot be eliminated in dimensions $d \ge 2$. In particular, we prove that any online spanner algorithm for a sequence of $n$ points in $\mathbb{R}^d$ under the $L_2$ norm has competitive ratio $Ω(f(n))$, where $\lim_{n\rightarrow \infty}f(n)=\infty$. Finally, we provide improved lower bounds under the $L_1$ norm: $Ω(\varepsilon^{-2}/\log \varepsilon^{-1})$ in the plane and $Ω(\varepsilon^{-d})$ in $\mathbb{R}^d$ for $d\geq 3$.