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

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
Jina AI
Jina AI
Hugging Face - Blog
Hugging Face - Blog
博客园 - 三生石上(FineUI控件)
博客园 - 【当耐特】
大猫的无限游戏
大猫的无限游戏
IT之家
IT之家
宝玉的分享
宝玉的分享
WordPress大学
WordPress大学
有赞技术团队
有赞技术团队
Apple Machine Learning Research
Apple Machine Learning Research
酷 壳 – CoolShell
酷 壳 – CoolShell
阮一峰的网络日志
阮一峰的网络日志
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
爱范儿
爱范儿
小众软件
小众软件
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
The Cloudflare Blog
S
SegmentFault 最新的问题
博客园 - Franky
博客园_首页
T
Tailwind CSS Blog
雷峰网
雷峰网
罗磊的独立博客

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
Performance of a greedy algorithm for edge covering by cl...
Gabrio Caimi, Holger Flier, Martin Fuchsberger, Marc Nunkesser · 2008-12-11 · via cs.DS updates on arXiv.org

In this paper a greedy algorithm to detect conflict cliques in interval graphs and circular-arc graphs is analyzed. In a graph, a stable set requires that at most one vertex is chosen for each edge. It is equivalent to requiring that at most one vertex for each maximal clique is chosen. We show that this algorithm finds all maximal cliques for interval graphs, i.e. it can compute the convex hull of the stable set polytope. In case of circular-arc graphs, the algorithm is not able to detect all maximal cliques, yet remaining correct. This problem occurs in the context of railway scheduling. A train requests the allocation of a railway infrastructure resource for a specific time interval. As one is looking for conflict-free train schedules, the used resource allocation intervals in a schedule must not overlap. The conflict-free choices of used intervals for each resource correspond to stable sets in the interval graph associated to the allocation time intervals.