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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
Shorter Labels for Routing in Trees
Paweł Gawrychowski, Wojciech Janczewski, Jakub Łopuszański · 2020-03-15 · via cs.DS updates on arXiv.org

A routing labeling scheme assigns a binary string, called a label, to each node in a network, and chooses a distinct port number from $\{1,\ldots,d\}$ for every edge outgoing from a node of degree $d$. Then, given the labels of $u$ and $w$ and no other information about the network, it should be possible to determine the port number corresponding to the first edge on the shortest path from $u$ to $w$. In their seminal paper, Thorup and Zwick [SPAA 2001] designed several routing methods for general weighted networks. An important technical ingredient in their paper that according to the authors ``may be of independent practical and theoretical interest'' is a routing labeling scheme for trees of arbitrary degrees. For a tree on $n$ nodes, their scheme constructs labels consisting of $(1+o(1))\log n$ bits such that the sought port number can be computed in constant time. Looking closer at their construction, the labels consist of $\log n + O(\log n\cdot \log\log\log n / \log\log n)$ bits. Given that the only known lower bound is $\log n+Ω(\log\log n)$, a natural question that has been asked for other labeling problems in trees is to determine the asymptotics of the smaller-order term. We make the first (and significant) progress in 19 years on determining the correct second-order term for the length of a label in a routing labeling scheme for trees on $n$ nodes. We design such a scheme with labels of length $\log n+O((\log\log n)^{2})$. Furthermore, we modify the scheme to allow for computing the port number in constant time at the expense of slightly increasing the length to $\log n+O((\log\log n)^{3})$.