Elsevier Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation

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. Introduces the latest and advanced algorithms in decentralized optimization of networked control systems Proposes effective strategies for efficient execution and privacy preservation in the development of decentralized optimization algorithms Constructs the frameworks of convergence and complexity analysis, privacy, security proof, and performance evaluation Includes systematic detailed implementations on how decentralized optimization algorithms solve the problems in real world systems: smart grid systems, online learning systems, wireless sensor systems, etc. Helps readers develop their own novel, decentralized optimization algorithms