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
Property Testing for Cyclic Groups and Beyond
Francois Le Gall, Yuichi Yoshida · 2011-05-10 · via cs.DS updates on arXiv.org

This paper studies the problem of testing if an input (Gamma,*), where Gamma is a finite set of unknown size and * is a binary operation over Gamma given as an oracle, is close to a specified class of groups. Friedl et al. [Efficient testing of groups, STOC'05] have constructed an efficient tester using poly(log|Gamma|) queries for the class of abelian groups. We focus in this paper on subclasses of abelian groups, and show that these problems are much harder: Omega(|Gamma|^{1/6}) queries are necessary to test if the input is close to a cyclic group, and Omega(|Gamma|^c) queries for some constant c are necessary to test more generally if the input is close to an abelian group generated by k elements, for any fixed integer k>0. We also show that knowledge of the size of the ground set Gamma helps only for k=1, in which case we construct an efficient tester using poly(log|Gamma|) queries; for any other value k>1 the query complexity remains Omega(|Gamma|^c). All our upper and lower bounds hold for both the edit distance and the Hamming distance. These are, to the best of our knowledge, the first nontrivial lower bounds for such group-theoretic problems in the property testing model and, in particular, they imply the first exponential separations between the classical and quantum query complexities of testing closeness to classes of groups.