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

F
Fortinet All Blogs
Last Week in AI
Last Week in AI
IT之家
IT之家
A
About on SuperTechFans
M
MIT News - Artificial intelligence
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
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 - 三生石上(FineUI控件)
博客园 - 【当耐特】
V
Visual Studio Blog
Microsoft Security Blog
Microsoft Security Blog
博客园_首页
aimingoo的专栏
aimingoo的专栏
The Cloudflare Blog
Vercel News
Vercel News
博客园 - Franky
有赞技术团队
有赞技术团队
B
Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
GbyAI
GbyAI
量子位
云风的 BLOG
云风的 BLOG
T
Tailwind CSS 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
Disjoint Dominating and 2-Dominating Sets in Graphs: Hard...
Soumyashree Rana, Sounaka Mishra, Bhawani Sankar Panda · 2023-12-02 · via cs.DS updates on arXiv.org

A set $D \subseteq V$ of a graph $G=(V, E)$ is a dominating set of $G$ if each vertex $v\in V\setminus D$ is adjacent to at least one vertex in $D,$ whereas a set $D_2\subseteq V$ is a $2$-dominating (double dominating) set of $G$ if each vertex $v\in V \setminus D_2$ is adjacent to at least two vertices in $D_2.$ A graph $G$ is a $DD_2$-graph if there exists a pair ($D, D_2$) of dominating set and $2$-dominating set of $G$ which are disjoint. In this paper, we solve some open problems posed by M.Miotk, J.~Topp and P.{Ż}yli{ń}ski (Disjoint dominating and 2-dominating sets in graphs, Discrete Optimization, 35:100553, 2020) by giving approximation algorithms for the problem of determining a minimal spanning $DD_2$-graph of minimum size (Min-$DD_2$) with an approximation ratio of $3$; a minimal spanning $DD_2$-graph of maximum size (Max-$DD_2$) with an approximation ratio of $3$; and for the problem of adding minimum number of edges to a graph $G$ to make it a $DD_2$-graph (Min-to-$DD_2$) with an $O(\log n)$ approximation ratio. Furthermore, we prove that Min-$DD_2$ and Max-$DD_2$ are APX-complete for graphs with maximum degree $4$. We also show that Min-$DD_2$ and Max-$DD_2$ are approximable within a factor of $1.8$ and $1.5$ respectively, for any $3$-regular graph. Finally, we show the inapproximability result of Max-Min-to-$DD_2$ for bipartite graphs, that this problem can not be approximated within $n^{\frac{1}{6}-\varepsilon}$ for any $\varepsilon >0,$ unless P=NP.