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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
Coloring k-colorable graphs using relatively small palettes
Eran Halperin, Ram Nathaniel, Uri Zwick · 2001-05-21 · via cs.DS updates on arXiv.org

We obtain the following new coloring results: * A 3-colorable graph on $n$ vertices with maximum degree~$Δ$ can be colored, in polynomial time, using $O((Δ\logΔ)^{1/3} \cdot\log{n})$ colors. This slightly improves an $O((Δ^{{1}/{3}} \log^{1/2}Δ)\cdot\log{n})$ bound given by Karger, Motwani and Sudan. More generally, $k$-colorable graphs with maximum degree $Δ$ can be colored, in polynomial time, using $O((Δ^{1-{2}/{k}}\log^{1/k}Δ) \cdot\log{n})$ colors. * A 4-colorable graph on $n$ vertices can be colored, in polynomial time, using $\Ot(n^{7/19})$ colors. This improves an $\Ot(n^{2/5})$ bound given again by Karger, Motwani and Sudan. More generally, $k$-colorable graphs on $n$ vertices can be colored, in polynomial time, using $\Ot(n^{α_k})$ colors, where $α_5=97/207$, $α_6=43/79$, $α_7=1391/2315$, $α_8=175/271$, ... The first result is obtained by a slightly more refined probabilistic analysis of the semidefinite programming based coloring algorithm of Karger, Motwani and Sudan. The second result is obtained by combining the coloring algorithm of Karger, Motwani and Sudan, the combinatorial coloring algorithms of Blum and an extension of a technique of Alon and Kahale (which is based on the Karger, Motwani and Sudan algorithm) for finding relatively large independent sets in graphs that are guaranteed to have very large independent sets. The extension of the Alon and Kahale result may be of independent interest.