Convergence Rate Of Distributed Averaging Dynamics And Optimization In Networks
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| Author | : Angelia Nedić |
| Publisher | : |
| Total Pages | : 100 |
| Release | : 2015 |
| Genre | : Computer networks |
| ISBN | : 9781680830415 |
Recent advances in wired and wireless technology lead to the emergence of large-scale networks such as Internet, wireless mobile ad-hoc networks, swarm robotics, smart-grid, and smart-sensor networks. The advances gave rise to new applications in networks including decentralized resource allocation in multi-agent systems, decentralized control of multi-agent systems, collaborative decision making, decentralized learning and estimation, and decentralized in-network signal processing. The advances also gave birth to new large cyber-physical systems such as sensor and social networks. These network systems are typically spatially distributed over a large area and may consists of hundreds of agents in smart-sensor networks to millions of agents in social networks. As such, they do not possess a central coordinator or a central point for access to the complete system information. This lack of central entity makes the traditional (centralized) optimization and control techniques inapplicable, thus necessitating the development of new distributed computational models and algorithms to support efficient operations over such networks. This tutorial provides an overview of the convergence rate of distributed algorithms for coordination and its relevance to optimization in a system of autonomous agents embedded in a communication network, where each agent is aware of (and can communicate with) its local neighbors only. The focus is on distributed averaging dynamics for consensus problems and its role in consensus-based gradient methods for convex optimization problems, where the network objective function is separable across the constituent agents.
| Author | : Fabio Fagnani |
| Publisher | : Springer |
| Total Pages | : 145 |
| Release | : 2017-11-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3319680226 |
This book deals with averaging dynamics, a paradigmatic example of network based dynamics in multi-agent systems. The book presents all the fundamental results on linear averaging dynamics, proposing a unified and updated viewpoint of many models and convergence results scattered in the literature. Starting from the classical evolution of the powers of a fixed stochastic matrix, the text then considers more general evolutions of products of a sequence of stochastic matrices, either deterministic or randomized. The theory needed for a full understanding of the models is constructed without assuming any knowledge of Markov chains or Perron–Frobenius theory. Jointly with their analysis of the convergence of averaging dynamics, the authors derive the properties of stochastic matrices. These properties are related to the topological structure of the associated graph, which, in the book’s perspective, represents the communication between agents. Special attention is paid to how these properties scale as the network grows in size. Finally, the understanding of stochastic matrices is applied to the study of other problems in multi-agent coordination: averaging with stubborn agents and estimation from relative measurements. The dynamics described in the book find application in the study of opinion dynamics in social networks, of information fusion in sensor networks, and of the collective motion of animal groups and teams of unmanned vehicles. Introduction to Averaging Dynamics over Networks will be of material interest to researchers in systems and control studying coordinated or distributed control, networked systems or multiagent systems and to graduate students pursuing courses in these areas.
| Author | : Angelia Nedić |
| Publisher | : Springer |
| Total Pages | : 317 |
| Release | : 2018-11-01 |
| Genre | : Business & Economics |
| ISBN | : 3319971425 |
This book contains three well-written research tutorials that inform the graduate reader about the forefront of current research in multi-agent optimization. These tutorials cover topics that have not yet found their way in standard books and offer the reader the unique opportunity to be guided by major researchers in the respective fields. Multi-agent optimization, lying at the intersection of classical optimization, game theory, and variational inequality theory, is at the forefront of modern optimization and has recently undergone a dramatic development. It seems timely to provide an overview that describes in detail ongoing research and important trends. This book concentrates on Distributed Optimization over Networks; Differential Variational Inequalities; and Advanced Decomposition Algorithms for Multi-agent Systems. This book will appeal to both mathematicians and mathematically oriented engineers and will be the source of inspiration for PhD students and researchers.
| Author | : Ziyang Meng |
| Publisher | : Springer Nature |
| Total Pages | : 169 |
| Release | : 2021-10-15 |
| Genre | : Science |
| ISBN | : 3030846822 |
This monograph provides a comprehensive exploration of new tools for modelling, analysis, and control of networked dynamical systems. Expanding on the authors’ previous work, this volume highlights how local exchange of information and cooperation among neighboring agents can lead to emergent global behaviors in a given networked dynamical system. Divided into four sections, the first part of the book begins with some preliminaries and the general networked dynamical model that is used throughout the rest of the book. The second part focuses on synchronization of networked dynamical systems, synchronization with non-expansive dynamics, periodic solutions of networked dynamical systems, and modulus consensus of cooperative-antagonistic networks. In the third section, the authors solve control problems with input constraint, large delays, and heterogeneous dynamics. The final section of the book is devoted to applications, studying control problems of spacecraft formation flying, multi-robot rendezvous, and energy resource coordination of power networks. Modelling, Analysis, and Control of Networked Dynamical Systems will appeal to researchers and graduate students interested in control theory and its applications, particularly those working in networked control systems, multi-agent systems, and cyber-physical systems. This volume can also be used in advanced undergraduate and graduate courses on networked control systems and multi-agent systems.
| Author | : Roberto Tempo |
| Publisher | : Springer |
| Total Pages | : 400 |
| Release | : 2018-02-24 |
| Genre | : Technology & Engineering |
| ISBN | : 3319670689 |
This book celebrates Professor Mathukumalli Vidyasagar’s outstanding achievements in systems, control, robotics, statistical learning, computational biology, and allied areas. The contributions in the book summarize the content of invited lectures given at the workshop “Emerging Applications of Control and Systems Theory” (EACST17) held at the University of Texas at Dallas in late September 2017 in honor of Professor Vidyasagar’s seventieth birthday. These contributions are the work of twenty-eight distinguished speakers from eight countries and are related to Professor Vidyasagar’s areas of research. This Festschrift volume will remain as a permanent scientific record of this event.
| Author | : Behrouz Touri |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 152 |
| Release | : 2012-03-02 |
| Genre | : Computers |
| ISBN | : 3642280021 |
The thesis deals with averaging dynamics in a multiagent networked system, which is a main mechanism for diffusing the information over such networks. It arises in a wide range of applications in engineered physical networks (such as mobile communication and sensor networks), as well as social and economic networks. The thesis provides in depth study of stability and other phenomena characterizing the limiting behavior of both deterministic and random averaging dynamics. By developing new concepts, and using the tools from dynamic system theory and non-negative matrix theory, several novel fundamental results are rigorously developed. These contribute significantly to our understanding of averaging dynamics as well as to non-negative random matrix theory. The exposition, although highly rigorous and technical, is elegant and insightful, and accompanied with numerous illustrative examples, which makes this thesis work easily accessible to those just entering this field and will also be much appreciated by experts in the field.
| Author | : Giuseppe Notarstefano |
| Publisher | : |
| Total Pages | : 148 |
| Release | : 2019-12-11 |
| Genre | : Technology & Engineering |
| ISBN | : 9781680836189 |
In an increasingly connected world, the term cyber-physical networks has been coined to refer to the communication among devices that is turning smart devices into smart (cooperating) systems. The distinctive feature of such systems is that significant advantage can be obtained if its interconnected, complex nature is exploited. Several challenges arising in cyber-physical networks can be stated as optimization problems. Examples are estimation, decision, learning and control applications. In cyber-physical networks, the goal is to design algorithms, based on the exchange of information among the processors, that take advantage of the aggregated computational power. Distributed Optimization for Smart Cyber-Physical Networks provides a comprehensive overview of the most common approaches used to design distributed optimization algorithms, together with the theoretical analysis of the main schemes in their basic version. It identifies and formalizes classes of problem set-ups that arise in motivating application scenarios. For each set-up, in order to give the main tools for analysis, tailored distributed algorithms in simplified cases are reviewed. Extensions and generalizations of the basic schemes are also discussed at the end of each chapter. Distributed Optimization for Smart Cyber-Physical Networks provides the reader with an accessible overview of the current research and gives important pointers towards new developments. It is an excellent starting point for research and students unfamiliar with the topic.
| Author | : Huiwei Wang |
| Publisher | : Springer Nature |
| Total Pages | : 227 |
| Release | : 2021-01-04 |
| Genre | : Technology & Engineering |
| ISBN | : 9813345284 |
This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.
| Author | : Huaqing Li |
| Publisher | : Springer Nature |
| Total Pages | : 243 |
| Release | : 2020-08-04 |
| Genre | : Technology & Engineering |
| ISBN | : 9811561095 |
This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, from a communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.
| Author | : Alberto Bemporad |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 373 |
| Release | : 2010-10-14 |
| Genre | : Mathematics |
| ISBN | : 0857290320 |
This book nds its origin in the WIDE PhD School on Networked Control Systems, which we organized in July 2009 in Siena, Italy. Having gathered experts on all the aspects of networked control systems, it was a small step to go from the summer school to the book, certainly given the enthusiasm of the lecturers at the school. We felt that a book collecting overviewson the important developmentsand open pr- lems in the eld of networked control systems could stimulate and support future research in this appealing area. Given the tremendouscurrentinterests in distributed control exploiting wired and wireless communication networks, the time seemed to be right for the book that lies now in front of you. The goal of the book is to set out the core techniques and tools that are ava- able for the modeling, analysis and design of networked control systems. Roughly speaking, the book consists of three parts. The rst part presents architectures for distributed control systems and models of wired and wireless communication n- works. In particular, in the rst chapter important technological and architectural aspects on distributed control systems are discussed. The second chapter provides insight in the behavior of communication channels in terms of delays, packet loss and information constraints leading to suitable modeling paradigms for commu- cation networks.
