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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 Approximating Partial Set Cover and Generalizations
Chandra Chekuri, Kent Quanrud, Zhao Zhang · 2019-07-10 · via cs.DS updates on arXiv.org

Partial Set Cover (PSC) is a generalization of the well-studied Set Cover problem (SC). In PSC the input consists of an integer $k$ and a set system $(U,S)$ where $U$ is a finite set, and $S \subseteq 2^U$ is a collection of subsets of $U$. The goal is to find a subcollection $S' \subseteq S$ of smallest cardinality such that sets in $S'$ cover at least $k$ elements of $U$; that is $|\cup_{A \in S'} A| \ge k$. SC is a special case of PSC when $k = |U|$. In the weighted version each set $X \in S$ has a non-negative weight $w(X)$ and the goal is to find a minimum weight subcollection to cover $k$ elements. Approximation algorithms for SC have been adapted to obtain comparable algorithms for PSC in various interesting cases. In recent work Inamdar and Varadarajan, motivated by geometric set systems, obtained a simple and elegant approach to reduce PSC to SC via the natural LP relaxation. They showed that if a deletion-closed family of SC admits a $β$-approximation via the natural LP relaxation, then one can obtain a $2(β+ 1)$-approximation for PSC on the same family. In a subsequent paper, they also considered a generalization of PSC that has multiple partial covering constraints which is partly inspired by and generalizes previous work of Bera et al on the Vertex Cover problem. Our main goal in this paper is to demonstrate some useful connections between the results in previous work and submodularity. This allows us to simplify, and in some cases improve their results. We improve the approximation for PSC to $(1-1/e)(β+ 1)$. We extend the previous work to the sparse setting.