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

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
大猫的无限游戏
大猫的无限游戏
博客园 - 聂微东
Jina AI
Jina AI
The Cloudflare Blog
V
Visual Studio Blog
博客园_首页
量子位
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 【当耐特】
爱范儿
爱范儿
博客园 - 三生石上(FineUI控件)
小众软件
小众软件
博客园 - 司徒正美
阮一峰的网络日志
阮一峰的网络日志
Last Week in AI
Last Week in AI
V
V2EX
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
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
WordPress大学
WordPress大学
宝玉的分享
宝玉的分享
T
Tailwind CSS Blog
博客园 - Franky

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
Optimal Deterministic Rendezvous in Labeled Lines
Yann Bourreau, Ananth Narayanan, Alexandre Nolin · 2025-05-08 · via cs.DS updates on arXiv.org

In a rendezvous task, some mobile agents dispersed in a network have to gather at an arbitrary common site. We consider the rendezvous problem on the infinite labeled line, with $2$ agents, without communication, and a synchronous notion of time. Each node on the line is labeled with a unique positive integer. The initial distance between the agents is denoted by $D$. Time is divided into rounds and measured from the moment an agent first wakes up. We denote by $τ$ the delay between the two agents' wake up times. If awake in a given round $T$, an agent at a node $v$ has three options: stay at the node $v$, take port $0$, or take port $1$. If it decides to stay, the agent will still be at node $v$ in round $T+1$. Otherwise, it will be at one of the two neighbors of $v$ on the infinite line, depending on the port it chose. The agents achieve rendezvous in $T$ rounds if they are at the same node in round $T$. We aim for a deterministic algorithm for this problem. The problem was recently considered by Miller and Pelc [Distributed Computing 2025]. With $\ell_{\max}$ the largest label of the two starting nodes, they showed that no algorithm can guarantee rendezvous in $o(D \log^* \ell_{\max})$ rounds. The lower bound follows from a connection with the LOCAL model of distributed computing, and holds even if the agents are guaranteed simultaneous wake-up ($τ= 0$) and are told their initial distance $D$. Miller and Pelc also gave an algorithm of optimal matching complexity $O(D \log^* \ell_{\max})$ when the agents know $D$, but only obtained the higher bound of $O(D^2 (\log^* \ell_{\max})^3)$ when $D$ is unknown to the agents. We improve this complexity to a tight $O(D \log^* \ell_{\max})$. In fact, our algorithm achieves rendezvous in $O(D \log^* \ell_{\min})$ rounds, where $\ell_{\min}$ is the smallest label within distance $O(D)$ of the two starting positions.