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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. xi 7. Acknowledgments - p. xiii 8. Chapter 1: Asynchronous decentralized algorithms for resource allocation in directed networks - p. 1 9. Chapter 2: Event-triggered decentralized accelerated algorithms for economic dispatch in networks - p. 43 10. Chapter 3: Variance-reduced decentralized projection algorithms for constrained optimization in networks - p. 71 11. Chapter 4: Event-triggered decentralized gradient tracking algorithms for stochastic optimization in networks - p. 99 12. Chapter 5: Differentially private decentralized dual averaging algorithms for online optimization in directed networks - p. 127 13. Chapter 6: Differentially private decentralized zeroth-order algorithms for online optimization in dynamic networks - p. 157 14. Chapter 7: Privacy-preserving decentralized dual averaging push algorithms with correlated perturbations - p. 195 15. Chapter 8: Privacy-preserving decentralized optimal economic dispatch algorithms with conditional noises - p. 223 16. Index - p. 257
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Preface - p. xi 7. Acknowledgments - p. xiii 8. Chapter 1: Asynchronous decentralized algorithms for resource allocation in directed networks - p. 1 9. Chapter 2: Event-triggered decentralized accelerated algorithms for economic dispatch in networks - p. 43 10. Chapter 3: Variance-reduced decentralized projection algorithms for constrained optimization in networks - p. 71 11. Chapter 4: Event-triggered decentralized gradient tracking algorithms for stochastic optimization in networks - p. 99 12. Chapter 5: Differentially private decentralized dual averaging algorithms for online optimization in directed networks - p. 127 13. Chapter 6: Differentially private decentralized zeroth-order algorithms for online optimization in dynamic networks - p. 157 14. Chapter 7: Privacy-preserving decentralized dual averaging push algorithms with correlated perturbations - p. 195 15. Chapter 8: Privacy-preserving decentralized optimal economic dispatch algorithms with conditional noises - p. 223 16. Index - p. 257
