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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
Online Rounding for Set Cover under Subset Arrivals
Jarosław Byrka, Yongho Shin · 2025-07-17 · via cs.DS updates on arXiv.org

A rounding scheme for set cover has served as an important component in design of approximation algorithms for the problem, and there exists an H_s-approximate rounding scheme, where s denotes the maximum subset size, directly implying an approximation algorithm with the same approximation guarantee. A rounding scheme has also been considered under some online models, and in particular, under the element arrival model used as a crucial subroutine in algorithms for online set cover, an O(log s)-competitive rounding scheme is known [Buchbinder, Chen, and Naor, SODA 2014]. On the other hand, under a more general model, called the subset arrival model, only a simple O(log n)-competitive rounding scheme is known, where n denotes the number of elements in the ground set. In this paper, we present an O(log^2 s)-competitive rounding scheme under the subset arrival model, with one mild assumption that s is known upfront. Using our rounding scheme, we immediately obtain an O(log^2 s)-approximation algorithm for multi-stage stochastic set cover, improving upon the existing algorithms [Swamy and Shmoys, SICOMP 2012; Byrka and Srinivasan, SIDMA 2018] when s is small enough compared to the number of stages and the number of elements. Lastly, for set cover with s = 2, also known as edge cover, we present a 1.8-competitive rounding scheme under the edge arrival model.