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

博客园 - 三生石上(FineUI控件)
Y
Y Combinator Blog
aimingoo的专栏
aimingoo的专栏
B
Blog
小众软件
小众软件
D
DataBreaches.Net
T
The Blog of Author Tim Ferriss
L
LangChain Blog
WordPress大学
WordPress大学
罗磊的独立博客
GbyAI
GbyAI
S
SegmentFault 最新的问题
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
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
T
Tailwind CSS Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
月光博客
月光博客
IT之家
IT之家
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
雷峰网
雷峰网
美团技术团队
F
Fortinet All Blogs
Hugging Face - Blog
Hugging Face - Blog
The GitHub Blog
The GitHub Blog

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
Undirected Multicast Network Coding Gaps via Locally Deco...
Mark Braverman, Zhongtian He · 2025-10-21 · via cs.DS updates on arXiv.org

The network coding problem asks whether data throughput in a network can be increased using coding (compared to treating bits as commodities in a flow). While it is well-known that a network coding advantage exists in directed graphs, the situation in undirected graphs is much less understood -- in particular, despite significant effort, it is not even known whether network coding is helpful at all for unicast sessions. In this paper we study the multi-source multicast network coding problem in undirected graphs. There are $k$ sources broadcasting each to a subset of nodes in a graph of size $n$. The corresponding combinatorial problem is a version of the Steiner tree packing problem, and the network coding question asks whether the multicast coding rate exceeds the tree-packing rate. We give the first super-constant bound to this problem, demonstrating an example with a coding advantage of $Ω(\log k)$. In terms of graph size, we obtain a lower bound of $2^{\tildeΩ(\sqrt{\log \log n})}$. We also obtain an upper bound of $O(\log n)$ on the gap. Our main technical contribution is a new reduction that converts locally-decodable codes in the low-error regime into multicast coding instances. This gives rise to a new family of explicitly constructed graphs, which may have other applications.