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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
Simple strategies versus optimal schedules in multi-agent...
Akitoshi Kawamura, Makoto Soejima · 2014-11-25 · via cs.DS updates on arXiv.org

Suppose that a set of mobile agents, each with a predefined maximum speed, want to patrol a fence together so as to minimize the longest time interval during which a point on the fence is left unvisited. In 2011, Czyzowicz, Gąsieniec, Kosowski and Kranakis studied this problem for the settings where the fence is an interval (a line segment) and a circle, and conjectured that the following simple strategies are always optimal: for Interval Patrolling, the simple strategy partitions the fence into subintervals, one for each agent, and lets each agent move back and forth in the assigned subinterval with its maximum speed; for Circle Patrolling, the simple strategy is to choose a number r, place the r fastest agents equidistantly around the circle, and move them at the speed of the rth agent. Surprisingly, these conjectures were then proved false: schedules were found (for some settings of maximum speeds) that slightly outperform the simple strategies. In this paper, we are interested in the ratio between the performances of optimal schedules and simple strategies. For the two problems, we construct schedules that are 4/3 times (for Interval Patrolling) and 21/20 times (for Circle Patrolling) as good, respectively, as the simple strategies. We also propose a new variant, in which we want to patrol a single point under the constraint that each agent can only visit the point some predefined time after its previous visit. We obtain some similar ratio bounds and NP-hardness results related to this problem.