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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
Dynamic $(Δ+ 1)$ Vertex Coloring
Noam Benson-Tilsen · 2026-01-12 · via cs.DS updates on arXiv.org

Several recent results from dynamic and sublinear graph coloring are surveyed. This problem is widely studied and has motivating applications like network topology control, constraint satisfaction, and real-time resource scheduling. Graph coloring algorithms are called colorers. In §1 are defined graph coloring, the dynamic model, and the notion of performance of graph algorithms in the dynamic model. In particular $(Δ+ 1)$-coloring, sublinear performance, and oblivious and adaptive adversaries are noted and motivated. In §2 the pair of approximately optimal dynamic vertex colorers given in arXiv:1708.09080 are summarized as a warmup for the $(Δ+ 1)$-colorers. In §3 the state of the art in dynamic $(Δ+ 1)$-coloring is presented. This section comprises a pair of papers (arXiv:1711.04355 and arXiv:1910.02063) that improve dynamic $(Δ+ 1)$-coloring from the naive algorithm with $O(Δ)$ expected amortized update time to $O(\log Δ)$, then to $O(1)$ with high probability. In §4 the results in arXiv:2411.04418, which gives a sublinear algorithm for $(Δ+ 1)$-coloring that generalizes oblivious adversaries to adaptive adversaries, are presented.