Decentralized Optimization in Networks
Algorithmic Efficiency and Privacy Preservation
Paperback Engels 2025 9780443333378Samenvatting
Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and solutions to decentralized optimization problems. The book demonstrates the application of decentralized optimization algorithms to enhance communication and computational efficiency, solve large-scale datasets, maintain privacy preservation, and address challenges in complex decentralized networks. The book covers key topics such as event-triggered communication, random link failures, zeroth-order gradients, variance-reduction, Polyak’s projection, stochastic gradient, random sleep, and differential privacy. It also includes simulations and practical examples to illustrate the algorithms' effectiveness and applicability in real-world scenarios.
Specificaties
Lezersrecensies
Inhoudsopgave
2. Event-Triggered Decentralized Accelerated Algorithms for Economic Dispatch in Networks
3. Variance-Reduced Decentralized Projection Algorithms for Constrained Optimization in Networks
4. Event-Triggered Decentralized Gradient Tracking Algorithms for Stochastic Optimization in Networks
5. Differentially Private Decentralized Dual Averaging Algorithms for Online Optimization in Directed Networks
6. Differentially Private Decentralized Zeroth-Order Algorithms for Online Optimization in Dynamic Networks
7. Privacy-Preserving Decentralized Dual Averaging Push Algorithms with Correlated Perturbations
8. Privacy-Preserving Decentralized Optimal Economic Dispatch Algorithms with Conditional Noises
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