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

J
Java Code Geeks
美团技术团队
Microsoft Azure Blog
Microsoft Azure Blog
V
Visual Studio Blog
Jina AI
Jina AI
博客园_首页
M
MIT News - Artificial intelligence
D
DataBreaches.Net
L
LangChain Blog
宝玉的分享
宝玉的分享
F
Fortinet All Blogs
A
About on SuperTechFans
月光博客
月光博客
Stack Overflow Blog
Stack Overflow Blog
Google DeepMind News
Google DeepMind News
N
Netflix TechBlog - Medium
Y
Y Combinator Blog
腾讯CDC
Vercel News
Vercel News
雷峰网
雷峰网
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
阮一峰的网络日志
阮一峰的网络日志
博客园 - 【当耐特】

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
Graph Motif Problems Parameterized by Dual
Guillaume Fertin, Christian Komusiewicz · 2019-08-11 · via cs.DS updates on arXiv.org

Let $G=(V,E)$ be a vertex-colored graph, where $C$ is the set of colors used to color $V$. The Graph Motif (or GM) problem takes as input $G$, a multiset $M$ of colors built from $C$, and asks whether there is a subset $S\subseteq V$ such that (i) $G[S]$ is connected and (ii) the multiset of colors obtained from $S$ equals $M$. The Colorful Graph Motif (or CGM) problem is the special case of GM in which $M$ is a set, and the List-Colored Graph Motif (or LGM) problem is the extension of GM in which each vertex $v$ of $V$ may choose its color from a list $\mathcal{L}(v)\subseteq C$ of colors. We study the three problems GM, CGM, and LGM, parameterized by the dual parameter $\ell:=|V|-|M|$. For general graphs, we show that, assuming the strong exponential time hypothesis, CGM has no $(2-ε)^\ell\cdot |V|^{\mathcal{O}(1)}$-time algorithm, which implies that a previous algorithm, running in $\mathcal{O}(2^\ell\cdot |E|)$ time is optimal [Betzler et al., IEEE/ACM TCBB 2011]. We also prove that LGM is W[1]-hard with respect to $\ell$ even if we restrict ourselves to lists of at most two colors. If we constrain the input graph to be a tree, then we show that GM can be solved in $\mathcal{O}(3^\ell\cdot |V|)$ time but admits no polynomial-size problem kernel, while CGM can be solved in $\mathcal{O}(\sqrt{2}^{\ell} + |V|)$ time and admits a polynomial-size problem kernel.