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

Martin Fowler
Martin Fowler
Microsoft Security Blog
Microsoft Security Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Vercel News
Vercel News
Y
Y Combinator Blog
D
DataBreaches.Net
IT之家
IT之家
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园_首页
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
WordPress大学
WordPress大学
H
Help Net Security
GbyAI
GbyAI
C
Check Point Blog
L
LangChain Blog
小众软件
小众软件
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
G
Google Developers Blog
月光博客
月光博客
V
V2EX
M
MIT News - Artificial intelligence
博客园 - 叶小钗

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
Refined Vertex Sparsifiers of Planar Graphs
Robert Krauthgamer</name> <arxiv:affiliation>Inbal</arxiv: · 2017-02-20 · via cs.DS updates on arXiv.org

We study the following version of cut sparsification. Given a large edge-weighted network $G$ with $k$ terminal vertices, compress it into a smaller network $H$ with the same terminals, such that every minimum terminal cut in $H$ approximates the corresponding one in $G$, up to a factor $q\geq 1$ that is called the quality. (The case $q=1$ is known also as a mimicking network). We provide new insights about the structure of minimum terminal cuts, leading to new results for cut sparsifiers of planar graphs. Our first contribution identifies a subset of the minimum terminal cuts, which we call elementary, that generates all the others. Consequently, $H$ is a cut sparsifier if and only if it preserves all the elementary terminal cuts (up to this factor $q$). This structural characterization lead to improved bounds on the size of $H$. For example, it improve the bound of mimicking-network size for planar graphs into a near-optimal one. Our second and main contribution is to refine the known bounds in terms of $γ=γ(G)$, which is defined as the minimum number of faces that are incident to all the terminals in a planar graph $G$. We prove that the number of elementary terminal cuts is $O((2k/γ)^{2γ})$ (compared to $O(2^k)$ terminal cuts), and furthermore obtain a mimicking-network of size $O(γ2^{2γ} k^4)$, which is near-optimal as a function of $γ$. In the analysis we break the elementary terminal cuts into fragments, and count them carefully. Our third contribution is a duality between cut sparsification and distance sparsification for certain planar graphs, when the sparsifier $H$ is required to be a minor of $G$. This duality connects problems that were previously studied separately, implying new results, new proofs of known results, and equivalences between open gaps.