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

A
About on SuperTechFans
有赞技术团队
有赞技术团队
人人都是产品经理
人人都是产品经理
月光博客
月光博客
美团技术团队
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
WordPress大学
WordPress大学
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
博客园_首页
爱范儿
爱范儿
G
Google Developers Blog
aimingoo的专栏
aimingoo的专栏
T
The Blog of Author Tim Ferriss
MongoDB | Blog
MongoDB | Blog
小众软件
小众软件
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
I
InfoQ
B
Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
大猫的无限游戏
大猫的无限游戏
T
Tailwind CSS Blog
F
Fortinet All Blogs

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
A Faster Parameterized Algorithm for Temporal Matching
Philipp Zschoche · 2020-10-21 · via cs.DS updates on arXiv.org

A temporal graph is a sequence of graphs (called layers) over the same vertex set -- describing a graph topology which is subject to discrete changes over time. A $Δ$-temporal matching $M$ is a set of time edges $(e,t)$ (an edge $e$ paired up with a point in time $t$) such that for all distinct time edges $(e,t),(e',t') \in M$ we have that $e$ and $e'$ do not share an endpoint, or the time-labels $t$ and $t'$ are at least $Δ$ time units apart. Mertzios et al. [STACS '20] provided a $2^{O(Δν)}\cdot |{\mathcal G}|^{O(1)}$-time algorithm to compute the maximum size of a $Δ$-temporal matching in a temporal graph $\mathcal G$, where $|\mathcal G|$ denotes the size of $\mathcal G$, and $ν$ is the $Δ$-vertex cover number of $\mathcal G$. The $Δ$-vertex cover number is the minimum number $ν$ such that the classical vertex cover number of the union of any $Δ$ consecutive layers of the temporal graph is upper-bounded by $ν$. We show an improved algorithm to compute a $Δ$-temporal matching of maximum size with a running time of $Δ^{O(ν)}\cdot |\mathcal G|$ and hence provide an exponential speedup in terms of $Δ$.