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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
Energy-Efficient Aggregation and Minimum-Degree Spanning ...
Yi-Jun Chang, Yang Ze Guan · 2026-05-29 · via cs.DS updates on arXiv.org

We study the aggregation problem in synchronous multi-hop radio networks with $O(\log n)$-bit messages and no collision detection. Each node initially holds a value, and the goal is to compute a global aggregate such as the sum of all values. Aggregation tasks arise naturally in wireless sensor networks, where nodes are often battery-powered and radio activity is the dominant source of energy consumption. Accordingly, our main objective is to minimize the energy complexity, defined as the maximum number of rounds in which any node is awake. Our main result is a randomized distributed algorithm that, with high probability, constructs and executes an aggregation schedule in $O(n \operatorname{polylog} n)$ rounds and using $O(Δ^\ast \operatorname{polylog} n)$ energy, where $Δ^\ast$ is the minimum possible maximum degree of a spanning tree of the network graph. This guarantee is nearly optimal: for any aggregation schedule and any graph, there exists a node that must be awake for at least $Δ^\ast$ rounds. As a by-product, the algorithm also computes a spanning tree whose maximum degree is within an $O(\log n)$ factor of $Δ^\ast$, with the same round and energy guarantees. For every tree edge, both endpoints learn that the edge belongs to the tree.