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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
Efficient Approximation Algorithms for Multi-Antennae Lar...
Longkun Guo, Hong Shen, Wenxing Zhu · 2015-04-18 · via cs.DS updates on arXiv.org

In a mobile network, wireless data broadcast over $m$ channels (frequencies) is a powerful means for distributed dissemination of data to clients who access the channels through multi-antennae equipped on their mobile devices. The $δ$-antennae largest weight data retrieval ($δ$ALWDR) problem is to compute a schedule for downloading a subset of data items that has a maximum total weight using $δ$ antennae in a given time interval. In this paper, we propose a ratio $1-\frac{1}{e}-ε$ approximation algorithm for the $δ$-antennae largest weight data retrieval ($δ$ALWDR) problem that has the same ratio as the known result but a significantly improved time complexity of $O(2^{\frac{1}ε}\frac{1}εm^{7}T^{3.5}L)$ from $O(ε^{3.5}m^{\frac{3.5}ε}T^{3.5}L)$ when $δ=1$ \cite{lu2014data}. To our knowledge, our algorithm represents the first ratio $1-\frac{1}{e}-ε$ approximation solution to $δ$ALWDR for the general case of arbitrary $δ$. To achieve this, we first give a ratio $1-\frac{1}{e}$ algorithm for the $γ$-separated $δ$ALWDR ($δ$A$γ$LWDR) with runtime $O(m^{7}T^{3.5}L)$, under the assumption that every data item appears at most once in each segment of $δ$A$γ$LWDR, for any input of maximum length $L$ on $m$ channels in $T$ time slots. Then, we show that we can retain the same ratio for $δ$A$γ$LWDR without this assumption at the cost of increased time complexity to $O(2^γm^{7}T^{3.5}L)$. This result immediately yields an approximation solution of same ratio and time complexity for $δ$ALWDR, presenting a significant improvement of the known time complexity of ratio $1-\frac{1}{e}-ε$ approximation to the problem.