Distributed Optimization and Learning: A Control-Theoretic Perspective

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Abstract

Multi-agent systems have emerged as an attractive and exciting area of research in the last two decades and have been widely used in real-world applications, including wheeled robotic systems, satellites, autonomous underwater vehicles, unmanned aerial vehicles, sensor networks and smart grids. Optimization serves as the most fundamental element in promoting efficiency and productivity across various sectors in manufacturing, agriculture, social science and economics, etc. The advent of distributed optimization algorithms, which use localized decision-making to achieve global goals through network communication, marks a significant leap in the application of multi-agent systems. These systems are predicated on the foundational elements of control theory, yet the full spectrum of control-theoretic principles as they apply to distributed optimization and learning is a frontier still worth for exploration. Despite the wealth of knowledge contributed by the control community, the profound insights and underlying motivations that drive these algorithms beckon for a more thorough and systematic investigation.

While existing monographs have delved into the structures, algorithms, and applications of distributed optimization and learning, there remains a pressing need for a cohesive framework that elucidates the fundamental tenets undergirding these complex topics. Bridging this knowledge gap, our book emerges as a comprehensive compendium that presents a thorough, self-contained exposition of distributed optimization and learning algorithms through the lens of control theory. At the heart of this book is the endeavor to uncover how control-theoretic methodologies can be harnessed to unravel and tackle distributed optimization and learning challenges within networked multi-agent systems. We uncover the profound connections between optimization and learning, crafting a unified theoretical platform that offers both deep understanding and practical guidance for the deployment of distributed optimization and learning algorithms. Our aim is to furnish readers with the insights and tools necessary to innovate and apply these algorithms across various domains, catalyzing progress and efficiency in the interconnected systems of tomorrow.

This book serves a broad audience, including academic researchers, graduate students, engineering professionals, and even infrastructure planners engaged in the strategic deployment of technological solutions in complex network environments like smart grids and urban traffic systems. By offering a cohesive treatment of distributed optimization and learning, this book not only fills an existing academic void but also enriches the toolkit available to professionals confronting the challenges of today's interconnected technological landscape. The structure of the book is thoughtfully designed to cater to both newcomers and seasoned researchers in the field.

The initial chapters are dedicated to the foundational concepts of distributed optimization and learning, introducing them at a moderate mathematical level suitable for students and professionals new to the domain. These chapters simplify complex theorems while retaining rigor and are supplemented by examples that illustrate key concepts clearly. As the book progresses, it delves into more complex subjects including cutting-edge research and advanced algorithms. These sections are aimed at graduate students and researchers looking to deepen their understanding of how foundational concepts are applied in real-world scenarios. The topics covered here are crucial for academic research and also serve as essential references for students engaged in related studies. Furthermore, the book addresses the needs of professionals in the engineering disciplines, whether they are involved in robotics, telecommunications, or energy sectors. It provides practical strategies and real-world examples that tackle the challenges faced in the design and implementation of systems. These applications demonstrate how distributed optimization and learning can be effectively employed across various industries to solve practical problems.
Original languageEnglish
PublisherAcademic Press
ISBN (Electronic)9780443216374
ISBN (Print)0443216371
Publication statusPublished - Jul 2024

Keywords

  • distributed optimization
  • distributed learning

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