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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
Distance Approximating Minors for Planar and Minor-Free G...
Hsien-Chih Chang, Jonathan Conroy · 2025-09-22 · via cs.DS updates on arXiv.org

Given an edge-weighted graph $G$ and a subset of vertices $T$ called terminals, an $α$-distance-approximating minor ($α$-DAM) of $G$ is a graph minor $H$ of $G$ that contains all terminals, such that the distance between every pair of terminals is preserved up to a factor of $α$. Distance-approximating minor would be an effective distance-sketching structure on minor-closed family of graphs; in the constant-stretch regime it generalizes the well-known Steiner Point Removal problem by allowing the existence of (a small number of) non-terminal vertices. Unfortunately, in the $(1+\varepsilon)$ regime the only known DAM construction for planar graphs relies on overlaying $\tilde{O}_\varepsilon(|T|)$ shortest paths in $G$, which naturally leads to a quadratic bound in the number of terminals [Cheung, Goranci, and Henzinger, ICALP 2016]. We break the quadratic barrier and build the first $(1+\varepsilon)$-distance-approximating minor for $k$-terminal planar graphs and minor-free graphs of near-linear size $\tilde{O}_\varepsilon(k)$. In addition to the near-optimality in size, the construction relies only on the existence of shortest-path separators [Abraham and Gavoille, PODC 2006] and $\varepsilon$-covers [Thorup, J.\ ACM 2004]. Consequently, this provides an alternative and simpler construction to the near-linear-size emulator for planar graphs [Chang, Krauthgamer, and Tan, STOC 2022], as well as the first near-linear-size emulator for minor-free graphs. Our DAM can be constructed in near-linear time.