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

aimingoo的专栏
aimingoo的专栏
博客园 - 三生石上(FineUI控件)
GbyAI
GbyAI
大猫的无限游戏
大猫的无限游戏
M
MIT News - Artificial intelligence
Microsoft Azure Blog
Microsoft Azure Blog
月光博客
月光博客
Engineering at Meta
Engineering at Meta
I
InfoQ
T
Tailwind CSS Blog
N
Netflix TechBlog - Medium
S
SegmentFault 最新的问题
H
Help Net Security
博客园 - 【当耐特】
WordPress大学
WordPress大学
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
美团技术团队
博客园 - 叶小钗
T
The Blog of Author Tim Ferriss
腾讯CDC
雷峰网
雷峰网
Martin Fowler
Martin Fowler
The GitHub Blog
The GitHub Blog
D
Docker

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
An FPTAS for Counting Proper Four-Colorings on Cubic Graphs
Pinyan Lu, Kuan Yang, Chihao Zhang, Minshen Zhu · 2016-11-13 · via cs.DS updates on arXiv.org

Graph coloring is arguably the most exhaustively studied problem in the area of approximate counting. It is conjectured that there is a fully polynomial-time (randomized) approximation scheme (FPTAS/FPRAS) for counting the number of proper colorings as long as $q \geq Δ+ 1$, where $q$ is the number of colors and $Δ$ is the maximum degree of the graph. The bound of $q = Δ+ 1$ is the uniqueness threshold for Gibbs measure on $Δ$-regular infinite trees. However, the conjecture remained open even for any fixed $Δ\geq 3$ (The cases of $Δ=1, 2$ are trivial). In this paper, we design an FPTAS for counting the number of proper $4$-colorings on graphs with maximum degree $3$ and thus confirm the conjecture in the case of $Δ=3$. This is the first time to achieve this optimal bound of $q = Δ+ 1$. Previously, the best FPRAS requires $q > \frac{11}{6} Δ$ and the best deterministic FPTAS requires $q > 2.581Δ+ 1$ for general graphs. In the case of $Δ=3$, the best previous result is an FPRAS for counting proper 5-colorings. We note that there is a barrier to go beyond $q = Δ+ 2$ for single-site Glauber dynamics based FPRAS and we overcome this by correlation decay approach. Moreover, we develop a number of new techniques for the correlation decay approach which can find applications in other approximate counting problems.