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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
Nibbling at Long Cycles: Dynamic (and Static) Edge Colori...
Sayan Bhattacharya, Martín Costa, Nadav Panski, Shay Solomon · 2023-11-07 · via cs.DS updates on arXiv.org

We consider the problem of maintaining a $(1+ε)Δ$-edge coloring in a dynamic graph $G$ with $n$ nodes and maximum degree at most $Δ$. The state-of-the-art update time is $O_ε(\text{polylog}(n))$, by Duan, He and Zhang [SODA'19] and by Christiansen [STOC'23], and more precisely $O(\log^7 n/ε^2)$, where $Δ= Ω(\log^2 n / ε^2)$. The following natural question arises: What is the best possible update time of an algorithm for this task? More specifically, \textbf{ can we bring it all the way down to some constant} (for constant $ε$)? This question coincides with the \emph{static} time barrier for the problem: Even for $(2Δ-1)$-coloring, there is only a naive $O(m \log Δ)$-time algorithm. We answer this fundamental question in the affirmative, by presenting a dynamic $(1+ε)Δ$-edge coloring algorithm with $O(\log^4 (1/ε)/ε^9)$ update time, provided $Δ= Ω_ε(\text{polylog}(n))$. As a corollary, we also get the first linear time (for constant $ε$) \emph{static} algorithm for $(1+ε)Δ$-edge coloring; in particular, we achieve a running time of $O(m \log (1/ε)/ε^2)$. We obtain our results by carefully combining a variant of the \textsc{Nibble} algorithm from Bhattacharya, Grandoni and Wajc [SODA'21] with the subsampling technique of Kulkarni, Liu, Sah, Sawhney and Tarnawski [STOC'22].