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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
Improved approximation algorithm for the Dense-3-Subhyper...
Amey Bhangale, Rajiv Gandhi, Guy Kortsarz · 2017-04-27 · via cs.DS updates on arXiv.org

The study of Dense-$3$-Subhypergraph problem was initiated in Chlamt{á}c et al. [Approx'16]. The input is a universe $U$ and collection ${\cal S}$ of subsets of $U$, each of size $3$, and a number $k$. The goal is to choose a set $W$ of $k$ elements from the universe, and maximize the number of sets, $S\in {\cal S}$ so that $S\subseteq W$. The members in $U$ are called {\em vertices} and the sets of ${\cal S}$ are called the {\em hyperedges}. This is the simplest extension into hyperedges of the case of sets of size $2$ which is the well known Dense $k$-subgraph problem. The best known ratio for the Dense-$3$-Subhypergraph is $O(n^{0.69783..})$ by Chlamt{á}c et al. We improve this ratio to $n^{0.61802..}$. More importantly, we give a new algorithm that approximates Dense-$3$-Subhypergraph within a ratio of $\tilde O(n/k)$, which improves the ratio of $O(n^2/k^2)$ of Chlamt{á}c et al. We prove that under the {\em log density conjecture} (see Bhaskara et al. [STOC'10]) the ratio cannot be better than $Ω(\sqrt{n})$ and demonstrate some cases in which this optimum can be attained.