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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 Hierarchy of Lower Bounds for Sublinear Additive Spanners
Amir Abboud, Greg Bodwin, Seth Pettie · 2016-07-26 · via cs.DS updates on arXiv.org

Spanners, emulators, and approximate distance oracles can be viewed as lossy compression schemes that represent an unweighted graph metric in small space, say $\tilde{O}(n^{1+δ})$ bits. There is an inherent tradeoff between the sparsity parameter $δ$ and the stretch function $f$ of the compression scheme, but the qualitative nature of this tradeoff has remained a persistent open problem. In this paper we show that the recent additive spanner lower bound of Abboud and Bodwin is just the first step in a hierarchy of lower bounds that fully characterize the asymptotic behavior of the optimal stretch function $f$ as a function of $δ\in (0,1/3)$. Specifically, for any integer $k\ge 2$, any compression scheme with size $O(n^{1+\frac{1}{2^k-1} - ε})$ has a sublinear additive stretch function $f$: $$f(d) = d + Ω(d^{1-\frac{1}{k}}).$$ This lower bound matches Thorup and Zwick's (2006) construction of sublinear additive emulators. It also shows that Elkin and Peleg's $(1+ε,β)$-spanners have an essentially optimal tradeoff between $δ,ε,$ and $β$, and that the sublinear additive spanners of Pettie (2009) and Chechik (2013) are not too far from optimal. To complement these lower bounds we present a new construction of $(1+ε, O(k/ε)^{k-1})$-spanners with size $O((k/ε)^{h_k} kn^{1+\frac{1}{2^{k+1}-1}})$, where $h_k < 3/4$. This size bound improves on the spanners of Elkin and Peleg (2004), Thorup and Zwick (2006), and Pettie (2009). According to our lower bounds neither the size nor stretch function can be substantially improved.