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

博客园_首页
爱范儿
爱范儿
罗磊的独立博客
V
V2EX
量子位
Last Week in AI
Last Week in AI
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
Jina AI
Jina AI
博客园 - 叶小钗
小众软件
小众软件
博客园 - 【当耐特】
Y
Y Combinator Blog
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
T
Tailwind CSS Blog
博客园 - 聂微东
Microsoft Security Blog
Microsoft Security Blog
美团技术团队
P
Proofpoint News Feed
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
有赞技术团队
有赞技术团队
MongoDB | Blog
MongoDB | Blog
Recent Announcements
Recent Announcements
酷 壳 – CoolShell
酷 壳 – CoolShell

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
Faster and Enhanced Inclusion-Minimal Cograph Completion
Christophe Crespelle, Daniel Lokshtanov, Thi Ha Duong Phan, Eric · 2020-01-22 · via cs.DS updates on arXiv.org

We design two incremental algorithms for computing an inclusion-minimal completion of an arbitrary graph into a cograph. The first one is able to do so while providing an additional property which is crucial in practice to obtain inclusion-minimal completions using as few edges as possible : it is able to compute a minimum-cardinality completion of the neighbourhood of the new vertex introduced at each incremental step. It runs in $O(n+m')$ time, where $m'$ is the number of edges in the completed graph. This matches the complexity of the algorithm in [Lokshtanov, Mancini and Papadopoulos 2010] and positively answers one of their open questions. Our second algorithm improves the complexity of inclusion-minimal completion to $O(n+m\log^2 n)$ when the additional property above is not required. Moreover, we prove that many very sparse graphs, having only $O(n)$ edges, require $Ω(n^2)$ edges in any of their cograph completions. For these graphs, which include many of those encountered in applications, the improvement we obtain on the complexity scales as $O(n/\log^2 n)$.