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

Last Week in AI
Last Week in AI
GbyAI
GbyAI
Apple Machine Learning Research
Apple Machine Learning Research
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
N
Netflix TechBlog - Medium
量子位
aimingoo的专栏
aimingoo的专栏
D
Docker
F
Fortinet All Blogs
T
Tailwind CSS Blog
V
V2EX
D
DataBreaches.Net
Google DeepMind News
Google DeepMind News
MyScale Blog
MyScale Blog
G
Google Developers Blog
罗磊的独立博客
MongoDB | Blog
MongoDB | Blog
Microsoft Security Blog
Microsoft Security Blog
Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
博客园 - 三生石上(FineUI控件)
小众软件
小众软件
A
About on SuperTechFans
IT之家
IT之家

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
Sparsest Cut on Bounded Treewidth Graphs: Algorithms and ...
Anupam Gupta, Kunal Talwar, David Witmer · 2013-05-07 · via cs.DS updates on arXiv.org

We give a 2-approximation algorithm for Non-Uniform Sparsest Cut that runs in time $n^{O(k)}$, where $k$ is the treewidth of the graph. This improves on the previous $2^{2^k}$-approximation in time $\poly(n) 2^{O(k)}$ due to Chlamtáč et al. To complement this algorithm, we show the following hardness results: If the Non-Uniform Sparsest Cut problem has a $ρ$-approximation for series-parallel graphs (where $ρ\geq 1$), then the Max Cut problem has an algorithm with approximation factor arbitrarily close to $1/ρ$. Hence, even for such restricted graphs (which have treewidth 2), the Sparsest Cut problem is NP-hard to approximate better than $17/16 - ε$ for $ε> 0$; assuming the Unique Games Conjecture the hardness becomes $1/α_{GW} - ε$. For graphs with large (but constant) treewidth, we show a hardness result of $2 - ε$ assuming the Unique Games Conjecture. Our algorithm rounds a linear program based on (a subset of) the Sherali-Adams lift of the standard Sparsest Cut LP. We show that even for treewidth-2 graphs, the LP has an integrality gap close to 2 even after polynomially many rounds of Sherali-Adams. Hence our approach cannot be improved even on such restricted graphs without using a stronger relaxation.