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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
On the Space Usage of Approximate Distance Oracles with S...
Tsvi Kopelowitz, Ariel Korin, Liam Roditty · 2023-10-19 · via cs.DS updates on arXiv.org

For an undirected unweighted graph G = (V, E) with n vertices and m edges, let d(u, v) denote the distance from u in V to v in V in G. An (alpha, beta)-stretch approximate distance oracle (ADO) for G is a data structure that, given u, v in V, returns in constant time a value d-hat (u, v) such that d(u, v) <= d-hat (u, v) <= alpha * d(u, v) + beta, for some reals alpha > 1, beta. If beta = 0, we say that the ADO has stretch alpha. Thorup and Zwick (2005) showed that one cannot beat stretch 3 with subquadratic space (in terms of n) for general graphs. Patrascu and Roditty (2010) showed that one can obtain stretch 2 using O(m^(1/3)n^(4/3)) space, and so if m is subquadratic in n, then the space usage is also subquadratic. Moreover, Patrascu and Roditty (2010) showed that one cannot beat stretch 2 with subquadratic space even for graphs where m = O-tilde(n), based on the set-intersection hypothesis. In this paper, we investigate the minimum possible stretch achievable by an ADO as a function of the graph's maximum degree, a study motivated by the question of identifying the conditions under which an ADO can be stored with subquadratic space while still ensuring a sub-2 stretch. In particular, we show that if the maximum degree in G is Delta_G <= O(n^(1/k - epsilon)) for some 0 < epsilon <= 1/k, then there exists a (2, 1 - k)-stretch ADO for G that uses O-tilde(n^(2 - (k * epsilon) / 3)) space. For k = 2, this result implies a subquadratic sub-2 stretch ADO for graphs with Delta_G <= O(n^(1/2 - epsilon)). We provide tight lower bounds for the upper bound under the same set intersection hypothesis, showing that if Delta_G = Theta(n^(1/k)), a (2, 1 - k)-stretch ADO requires Omega-tilde(n^2) space. Moreover, we show that for constants epsilon, c > 0, a (2 - epsilon, c)-stretch ADO requires Omega-tilde(n^2) space even for graphs with Delta_G = Theta-tilde(1).