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

aimingoo的专栏
aimingoo的专栏
B
Blog RSS Feed
Recent Announcements
Recent Announcements
Vercel News
Vercel News
M
MIT News - Artificial intelligence
阮一峰的网络日志
阮一峰的网络日志
L
LangChain Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Security Blog
Microsoft Security Blog
H
Help Net Security
T
The Blog of Author Tim Ferriss
Y
Y Combinator Blog
G
Google Developers Blog
罗磊的独立博客
爱范儿
爱范儿
宝玉的分享
宝玉的分享
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
S
SegmentFault 最新的问题
WordPress大学
WordPress大学
月光博客
月光博客
人人都是产品经理
人人都是产品经理
Apple Machine Learning Research
Apple Machine Learning Research

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
Finding Triangles or Independent Sets; and Other Dual Pai...
Adrian Dumitrescu · 2021-05-04 · via cs.DS updates on arXiv.org

We revisit the algorithmic problem of finding a triangle in a graph (\textsc{Triangle Detection}), and examine its relation to other problems such as \textsc{3Sum}, \textsc{Independent Set}, and \textsc{Graph Coloring}. We obtain several new algorithms: \smallskip (I) A simple randomized algorithm for finding a triangle in a graph. As an application, we study the range of a conjecture of Pǎtraşcu (2010) regarding the triangle detection problem. \smallskip (II) An algorithm which given a graph $G=(V,E)$ performs one of the following tasks in $O(m+n)$ (ie, linear) time: (i)~compute a $Ω(1/\sqrt{n})$-approximation of a maximum independent set in $G$ or (ii)~find a triangle in $G$. The run-time is faster than that for any previous method for each of these tasks. \smallskip (III) An algorithm which given a graph $G=(V,E)$ performs one of the following tasks in $O(m+n^{3/2})$ time: (i)~compute an $\sqrt{n}$-approximation for \textsc{Graph Coloring} of $G$ or (ii)~find a triangle in $G$. The run-time is faster than that for any previous method for each of these tasks on dense graphs, with $m =ω(n^{9/8})$. \smallskip (IV) The second and third results suggest the following broader research direction: if it is difficult to find (A) or (B) separately, can one find one of the two efficiently? This motivates the \emph{dual pair} concept we introduce. We discuss and provide several instances of dual-pair approximation.