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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
A Tight Analysis of the Parallel Undecided-State Dynamics...
Andrea E. F. Clementi, Luciano Gualà, Francesco Pasquale, Giacom · 2017-07-17 · via cs.DS updates on arXiv.org

The \emph{Undecided-State Dynamics} is a well-known protocol for distributed consensus. We analyze it in the parallel \pull\ communication model on the complete graph for the \emph{binary} case (every node can either support one of \emph{two} possible colors, or be in the undecided state). An interesting open question is whether this dynamics \emph{always} (i.e., starting from an arbitrary initial configuration) reaches consensus \emph{quickly} (i.e., within a polylogarithmic number of rounds) in a complete graph with $n$ nodes. Previous work in this setting only considers initial color configurations with no undecided nodes and a large \emph{bias} (i.e., $Θ(n)$) towards the majority color. In this paper we present an \textit{unconditional} analysis of the Undecided-State Dynamics that answers to the above question in the affirmative. We prove that, starting from \textit{any} initial configuration, the process reaches a monochromatic configuration within $O(\log n)$ rounds, with high probability. This bound turns out to be tight. Our analysis also shows that, if the initial configuration has bias $Ω(\sqrt{n\log n})$, then the dynamics converges toward the initial majority color, with high probability.