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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
Distributed coloring of graphs with an optimal number of ...
Étienne Bamas, Louis Esperet · 2018-09-21 · via cs.DS updates on arXiv.org

This paper studies sufficient conditions to obtain efficient distributed algorithms coloring graphs optimally (i.e.\ with the minimum number of colors) in the LOCAL model of computation. Most of the work on distributed vertex coloring so far has focused on coloring graphs of maximum degree $Δ$ with at most $Δ+1$ colors (or $Δ$ colors when some simple obstructions are forbidden). When $Δ$ is sufficiently large and $c\ge Δ-k_Δ+1$, for some integer $k_Δ\approx \sqrtΔ-2$, we give a distributed algorithm that given a $c$-colorable graph $G$ of maximum degree $Δ$, finds a $c$-coloring of $G$ in $\min\{O((\logΔ)^{1/12}\log n), 2^{O(\log Δ+\sqrt{\log \log n})}\}$ rounds, with high probability. The lower bound $Δ-k_Δ+1$ is best possible in the sense that for infinitely many values of $Δ$, we prove that when $χ(G)\le Δ-k_Δ$, finding an optimal coloring of $G$ requires $Ω(n)$ rounds. Our proof is a light adaptation of a remarkable result of Molloy and Reed, who proved that for $Δ$ large enough, for any $c\ge Δ- k_Δ$ deciding whether $χ(G)\le c$ is in {\textsf{P}}, while Embden-Weinert \emph{et al.}\ proved that for $c\le Δ-k_Δ-1$, the same problem is {\textsf{NP}}-complete. Note that the sequential and distributed thresholds differ by one. We also show that for any sufficiently large $Δ$, and $Ω(\log Δ)\le k \le Δ/100$, every graph of maximum degree $Δ$ and clique number at most $Δ-k$ can be efficiently colored with at most $Δ-\varepsilon k$ colors, for some absolute constant $\varepsilon >0$, with a randomized algorithm running in $O(\log n/\log \log n)$ rounds with high probability.