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

爱范儿
爱范儿
腾讯CDC
博客园 - 司徒正美
A
About on SuperTechFans
H
Help Net Security
J
Java Code Geeks
C
Check Point Blog
B
Blog RSS Feed
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
MongoDB | Blog
MongoDB | Blog
U
Unit 42
Hugging Face - Blog
Hugging Face - Blog
Last Week in AI
Last Week in AI
MyScale Blog
MyScale Blog
V
Visual Studio Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
I
InfoQ
H
Hackread – Cybersecurity News, Data Breaches, AI and More
F
Fortinet All Blogs
博客园 - 聂微东
酷 壳 – CoolShell
酷 壳 – CoolShell
GbyAI
GbyAI
博客园 - 【当耐特】
雷峰网
雷峰网

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
Bridge Girth: A Unifying Notion in Network Design
Greg Bodwin, Gary Hoppenworth, Ohad Trabelsi · 2022-12-23 · via cs.DS updates on arXiv.org

A classic 1993 paper by Althőfer et al. proved a tight reduction from spanners, emulators, and distance oracles to the extremal function $γ$ of high-girth graphs. This paper initiated a large body of work in network design, in which problems are attacked by reduction to $γ$ or the analogous extremal function for other girth concepts. In this paper, we introduce and study a new girth concept that we call the bridge girth of path systems, and we show that it can be used to significantly expand and improve this web of connections between girth problems and network design. We prove two kinds of results: 1) We write the maximum possible size of an $n$-node, $p$-path system with bridge girth $>k$ as $β(n, p, k)$, and we write a certain variant for "ordered" path systems as $β^*(n, p, k)$. We identify several arguments in the literature that implicitly show upper or lower bounds on $β, β^*$, and we provide some polynomially improvements to these bounds. In particular, we construct a tight lower bound for $β(n, p, 2)$, and we polynomially improve the upper bounds for $β(n, p, 4)$ and $β^*(n, p, \infty)$. 2) We show that many state-of-the-art results in network design can be recovered or improved via black-box reductions to $β$ or $β^*$. Examples include bounds for distance/reachability preservers, exact hopsets, shortcut sets, the flow-cut gaps for directed multicut and sparsest cut, an integrality gap for directed Steiner forest. We believe that the concept of bridge girth can lead to a stronger and more organized map of the research area. Towards this, we leave many open problems, related to both bridge girth reductions and extremal bounds on the size of path systems with high bridge girth.