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

Recent Announcements
Recent Announcements
J
Java Code Geeks
雷峰网
雷峰网
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
腾讯CDC
博客园 - 司徒正美
B
Blog RSS Feed
博客园 - 三生石上(FineUI控件)
I
InfoQ
N
Netflix TechBlog - Medium
L
LangChain Blog
博客园_首页
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
T
Tailwind CSS Blog
MyScale Blog
MyScale Blog
美团技术团队
The Cloudflare Blog
爱范儿
爱范儿
Stack Overflow Blog
Stack Overflow Blog
博客园 - 聂微东
H
Help Net Security
Martin Fowler
Martin Fowler
V
Visual Studio 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
Almost Shortest Paths with Near-Additive Error in Weighte...
Michael Elkin, Yuval Gitlitz, Ofer Neiman · 2019-07-26 · via cs.DS updates on arXiv.org

Let $G=(V,E,w)$ be a weighted undirected graph with $n$ vertices and $m$ edges, and fix a set of $s$ sources $S\subseteq V$. We study the problem of computing {\em almost shortest paths} (ASP) for all pairs in $S \times V$ in both classical centralized and parallel (PRAM) models of computation. Consider the regime of multiplicative approximation of $1+ε$, for an arbitrarily small constant $ε> 0$ . In this regime existing centralized algorithms require $Ω(\min\{|E|s,n^ω\})$ time, where $ω< 2.372$ is the matrix multiplication exponent. Existing PRAM algorithms with polylogarithmic depth (aka time) require work $Ω(\min\{|E|s,n^ω\})$. Our centralized algorithm has running time $O((m+ ns)n^ρ)$, and its PRAM counterpart has polylogarithmic depth and work $O((m + ns)n^ρ)$, for an arbitrarily small constant $ρ> 0$. For a pair $(s,v) \in S\times V$, it provides a path of length $\hat{d}(s,v)$ that satisfies $\hat{d}(s,v) \le (1+ε)d_G(s,v) + β\cdot W(s,v)$, where $W(s,v)$ is the weight of the heaviest edge on some shortest $s-v$ path. Hence our additive term depends linearly on a {\em local} maximum edge weight, as opposed to the global maximum edge weight in previous works. Finally, our $β= (1/ρ)^{O(1/ρ)}$. We also extend a centralized algorithm of Dor et al. \cite{DHZ00}. For a parameter $κ= 1,2,\ldots$, this algorithm provides for {\em unweighted} graphs a purely additive approximation of $2(κ-1)$ for {\em all pairs shortest paths} (APASP) in time $\tilde{O}(n^{2+1/κ})$. Within the same running time, our algorithm for {\em weighted} graphs provides a purely additive error of $2(κ- 1) W(u,v)$, for every vertex pair $(u,v) \in {V \choose 2}$, with $W(u,v)$ defined as above. On the way to these results we devise a suit of novel constructions of spanners, emulators and hopsets.