Название: Distributed Optimization in Networked Systems: Algorithms and ApplicationsАвтор: Qingguo Lu, Xiaofeng Liao, Huaqing Li
Издательство: Springer
Серия: Wireless Networks
Год: 2023
Страниц: 282
Язык: английский
Формат: pdf (true), epub
Размер: 34.8 MB
This book focuses on improving the performance (convergence rate, communication efficiency, computational efficiency, etc.) of algorithms in the context of distributed optimization in networked systems and their successful application to real-world applications (smart grids and online learning). Readers may be particularly interested in the sections on consensus protocols, optimization skills, accelerated mechanisms, event-triggered strategies, variance-reduction communication techniques, etc., in connection with distributed optimization in various networked systems. This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike.
In recent years, the Internet of Things (IoT) and Big Data have been interconnected to a wide and deep extent through the sensing, computing, communication, and control of intelligent information. Networked systems are playing an increasingly important role in the interconnected information environment, profoundly affecting Computer Science, Artificial Intelligence, and other related fields. The core of such systems composed of many nodes is to efficiently accomplish certain global goals by collaborating with each other, while making separate decisions based on different preferences, thus solving large-scale complex problems that are difficult for individual nodes to perform, with strong resistance to interference and environmental adaptability. In addition, such systems require participating nodes to access only their own local information. This may be due to the consideration of security and privacy issues in the network, or simply because the network is too large, making the aggregation of global information to a central node practically impossible or very inefficient. Currently, as a hot research topic with wide applicability and great application value across multiple disciplines, distributed optimization of networked systems has laid an important foundation for promoting and leading the frontier development in Computer Science and Artificial Intelligence. However, networked systems cover a large number of intelligent devices (nodes), and the network environment is often dynamic and changing, making it extremely hard to optimize and analyze them. It is problematic for existing theories and methods to effectively address the new needs and challenges of optimization brought about by the rapid development of technologies related to networked systems. Hence, it is urgent to develop new theories and methods of distributed optimization over networks.
Analysis and synthesis including distributed unconstrained optimization, distributed constrained optimization, distributed nonsmooth optimization, distributed online optimization, distributed economic dispatch in smart grids, undirected networks, directed networks, time-varying networks, consensus control protocol, gradient tracking technique, event-triggered communication strategy, Nesterov and heavy-ball accelerated mechanisms, variance-reduction technique, differential privacy strategy, gradient descent algorithm, accelerated algorithm, stochastic gradient algorithm, and online algorithm are all thoroughly studied. This monograph mainly investigates distributed optimization algorithms and applications in networked control systems. In general, the following problems are investigated in this monograph: (1) accelerated algorithms for distributed convex optimization; (2) projection algorithms for distributed stochastic optimization; (3) proximal algorithms for distributed coupled optimization; (4) event-triggered algorithms for distributed convex optimization; (5) event-triggered acceleration algorithms for distributed stochastic optimization; (6) accelerated algorithms for distributed economic dispatch; (7) primal-dual algorithms for distributed economic dispatch; (8) event-triggered algorithms for distributed economic dispatch; and (9) privacy preserving algorithms for distributed online learning. Among the topics, simulation results including some typical real applications are presented to illustrate the effectiveness and the practicability of the distributed optimization algorithms proposed in the previous parts.
This book is appropriate as a college course textbook for undergraduate and graduate students majoring in computer science, automation, Artificial Intelligence, and electric engineering, and as a reference material for researchers and technologists in related fields.
Contents:
1. Accelerated Algorithms for Distributed Convex Optimization
2. Projection Algorithms for Distributed Stochastic Optimization
3. Proximal Algorithms for Distributed Coupled Optimization
4. Event-Triggered Algorithms for Distributed Convex Optimization
5. Event-Triggered Acceleration Algorithms for Distributed Stochastic Optimization
6. Accelerated Algorithms for Distributed Economic Dispatch
7. Primal–Dual Algorithms for Distributed Economic Dispatch
8. Event-Triggered Algorithms for Distributed Economic Dispatch
9. Privacy Preserving Algorithms for Distributed Online Learning
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