Decentralized Optimization in Networks provides the reader with theoretical foundations, practical guidance, and problem-solving approaches to decentralized optimization. It teaches how to apply decentralized optimization algorithms to improve optimization efficiency (communication efficiency, computational efficiency, fast convergence), solve large-scale problems (training for large-scale datasets), achieve privacy preservation (effectively counter external eavesdropping attacks, differential attacks, etc.), and overcome a range of challenges in complex decentralized network environments (random sleep, random link failures, time-varying, directed, etc.). It focuses on 1) communication-efficiency: event-triggered communication, random link failures, zeroth-order gradients. 2) computation-efficiency: variance-reduction techniques, Polyak's projection, stochastic gradient, random sleep. 3) privacy preservation: differential privacy, edge-based correlated perturbations, conditional noises. It uses simulation results, including practical application examples, to illustrate the effectiveness and the practicability of decentralized optimization algorithms.

Key Features, 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 and security proofing, 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 to develop their own novel decentralized optimization algorithms.