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

Engineering at Meta
Engineering at Meta
G
Google Developers Blog
WordPress大学
WordPress大学
M
MIT News - Artificial intelligence
D
DataBreaches.Net
云风的 BLOG
云风的 BLOG
爱范儿
爱范儿
Microsoft Security Blog
Microsoft Security Blog
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
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Blog — PlanetScale
Blog — PlanetScale
T
Tailwind CSS Blog
S
SegmentFault 最新的问题
阮一峰的网络日志
阮一峰的网络日志
博客园 - 三生石上(FineUI控件)
酷 壳 – CoolShell
酷 壳 – CoolShell
Recent Announcements
Recent Announcements
T
The Blog of Author Tim Ferriss
I
InfoQ
MyScale Blog
MyScale Blog
V
V2EX
B
Blog
罗磊的独立博客
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

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
Simultaneous Feedback Vertex Set: A Parameterized Perspec...
Akanksha Agrawal, Daniel Lokshtanov, Amer E. Mouawad, Saket Saur · 2015-10-06 · via cs.DS updates on arXiv.org

Given a family of graphs $\mathcal{F}$, a graph $G$, and a positive integer $k$, the $\mathcal{F}$-Deletion problem asks whether we can delete at most $k$ vertices from $G$ to obtain a graph in $\mathcal{F}$. $\mathcal{F}$-Deletion generalizes many classical graph problems such as Vertex Cover, Feedback Vertex Set, and Odd Cycle Transversal. A graph $G = (V, \cup_{i=1}^α E_{i})$, where the edge set of $G$ is partitioned into $α$ color classes, is called an $α$-edge-colored graph. A natural extension of the $\mathcal{F}$-Deletion problem to edge-colored graphs is the $α$-Simultaneous $\mathcal{F}$-Deletion problem. In the latter problem, we are given an $α$-edge-colored graph $G$ and the goal is to find a set $S$ of at most $k$ vertices such that each graph $G_i \setminus S$, where $G_i = (V, E_i)$ and $1 \leq i \leq α$, is in $\mathcal{F}$. In this work, we study $α$-Simultaneous $\mathcal{F}$-Deletion for $\mathcal{F}$ being the family of forests. In other words, we focus on the $α$-Simultaneous Feedback Vertex Set ($α$-SimFVS) problem. Algorithmically, we show that, like its classical counterpart, $α$-SimFVS parameterized by $k$ is fixed-parameter tractable (FPT) and admits a polynomial kernel, for any fixed constant $α$. In particular, we give an algorithm running in $2^{O(αk)}n^{O(1)}$ time and a kernel with $O(αk^{3(α+ 1)})$ vertices. The running time of our algorithm implies that $α$-SimFVS is FPT even when $α\in o(\log n)$. We complement this positive result by showing that for $α\in O(\log n)$, where $n$ is the number of vertices in the input graph, $α$-SimFVS becomes W[1]-hard. Our positive results answer one of the open problems posed by Cai and Ye (MFCS 2014).