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

Jina AI
Jina AI
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
有赞技术团队
有赞技术团队
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
U
Unit 42
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Recent Announcements
Recent Announcements
Y
Y Combinator Blog
Vercel News
Vercel News
Martin Fowler
Martin Fowler
V
V2EX
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
L
LangChain Blog
云风的 BLOG
云风的 BLOG
H
Hackread – Cybersecurity News, Data Breaches, AI and More
aimingoo的专栏
aimingoo的专栏
G
Google Developers Blog
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium
Google DeepMind News
Google DeepMind News
雷峰网
雷峰网
阮一峰的网络日志
阮一峰的网络日志
F
Fortinet All Blogs

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
Stackelberg Shortest Path Tree Game, Revisited
Sergio Cabello · 2012-07-10 · via cs.DS updates on arXiv.org

Let $G(V,E)$ be a directed graph with $n$ vertices and $m$ edges. The edges $E$ of $G$ are divided into two types: $E_F$ and $E_P$. Each edge of $E_F$ has a fixed price. The edges of $E_P$ are the priceable edges and their price is not fixed a priori. Let $r$ be a vertex of $G$. For an assignment of prices to the edges of $E_P$, the revenue is given by the following procedure: select a shortest path tree $T$ from $r$ with respect to the prices (a tree of cheapest paths); the revenue is the sum, over all priceable edges $e$, of the product of the price of $e$ and the number of vertices below $e$ in $T$. Assuming that $k=|E_P|\ge 2$ is a constant, we provide a data structure whose construction takes $O(m+n\log^{k-1} n)$ time and with the property that, when we assign prices to the edges of $E_P$, the revenue can be computed in $(\log^{k-1} n)$. Using our data structure, we save almost a linear factor when computing the optimal strategy in the Stackelberg shortest paths tree game of [D. Bil{ò} and L. Gual{à} and G. Proietti and P. Widmayer. Computational aspects of a 2-Player Stackelberg shortest paths tree game. Proc. WINE 2008].