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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
Faster Vizing and Near-Vizing Edge Coloring Algorithms
Sepehr Assadi · 2024-05-22 · via cs.DS updates on arXiv.org

Vizing's celebrated theorem states that every simple graph with maximum degree $Δ$ admits a $(Δ+1)$ edge coloring which can be found in $O(m \cdot n)$ time on $n$-vertex $m$-edge graphs. This is just one color more than the trivial lower bound of $Δ$ colors needed in any proper edge coloring. After a series of simplifications and variations, this running time was eventually improved by Gabow, Nishizeki, Kariv, Leven, and Terada in 1985 to $O(m\sqrt{n\log{n}})$ time. This has effectively remained the state-of-the-art modulo an $O(\sqrt{\log{n}})$-factor improvement by Sinnamon in 2019. As our main result, we present a novel randomized algorithm that computes a $Δ+O(\log{n})$ coloring of any given simple graph in $O(m\logΔ)$ expected time; in other words, a near-linear time randomized algorithm for a ``near''-Vizing's coloring. As a corollary of this algorithm, we also obtain the following results: * A randomized algorithm for $(Δ+1)$ edge coloring in $O(n^2\log{n})$ expected time. This is near-linear in the input size for dense graphs and presents the first polynomial time improvement over the longstanding bounds of Gabow et.al. for Vizing's theorem in almost four decades. * A randomized algorithm for $(1+\varepsilon) Δ$ edge coloring in $O(m\log{(1/\varepsilon)})$ expected time for any $\varepsilon = ω(\log{n}/Δ)$. The dependence on $\varepsilon$ exponentially improves upon a series of recent results that obtain algorithms with runtime of $Ω(m/\varepsilon)$ for this problem.