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Author: Qingguo Lü
Publisher: Springer Nature
ISBN: 9811985596
Category : Computers
Languages : en
Pages : 282
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Book Description
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.
Author: Qingguo Lü
Publisher: Springer Nature
ISBN: 9811985596
Category : Computers
Languages : en
Pages : 282
Get Book Here
Book Description
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.
Author: Yapeng Lu
Publisher: Open Dissertation Press
ISBN: 9781374706675
Category :
Languages : en
Pages :
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Book Description
This dissertation, "An Integrated Algorithm for Distributed Optimization in Networked Systems" by Yapeng, Lu, 呂亞鵬, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b4322423 Subjects: Business logistics - Data processing Wireless sensor networks Distributed artificial intelligence - Industrial applications Algorithms
Author: Björn Johansson
Publisher:
ISBN: 9789174151909
Category :
Languages : en
Pages : 188
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Book Description
Author: Yapeng Lu
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 206
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Book Description
Author: Huaqing Li
Publisher: Springer Nature
ISBN: 9811561095
Category : Technology & Engineering
Languages : en
Pages : 243
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Book Description
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: Zhongguo Li
Publisher: Elsevier
ISBN: 0443216371
Category : Technology & Engineering
Languages : en
Pages : 288
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Book Description
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes. Provides a series of the latest results, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocation Presents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniques Offers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches
Author: Ermin Wei
Publisher:
ISBN:
Category :
Languages : en
Pages : 179
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Book Description
In the interconnected world of today, large-scale multi-agent networked systems are ubiquitous. This thesis studies two classes of multi-agent systems, where each agent has local information and a local objective function. In the first class of systems, the agents are collaborative and the overall objective is to optimize the sum of local objective functions. This setup represents a general family of separable problems in large-scale multi-agent convex optimization systems, which includes the LASSO (Least-Absolute Shrinkage and Selection Operator) and many other important machine learning problems. We propose fast fully distributed both synchronous and asynchronous ADMM (Alternating Direction Method of Multipliers) based methods. Both of the proposed algorithms achieve the best known rate of convergence for this class of problems, O(1/k), where k is the number of iterations. This rate is the first rate of convergence guarantee for asynchronous distributed methods solving separable convex problems. For the synchronous algorithm, we also relate the rate of convergence to the underlying network topology. The second part of the thesis focuses on the class of systems where the agents are only interested in their local objectives. In particular, we study the market interaction in the electricity market. Instead of the traditional supply-follow-demand approach, we propose and analyze a systematic multi-period market framework, where both (price-taking) consumers and generators locally respond to price. We show that this new market interaction at competitive equilibrium is efficient and the improvement in social welfare over the traditional market can be unbounded. The resulting system, however, may feature undesirable price and generation fluctuations, which imposes significant challenges in maintaining reliability of the electricity grid. We first establish that the two fluctuations are positively correlated. Then in order to reduce both fluctuations, we introduce an explicit penalty on the price fluctuation. The penalized problem is shown to be equivalent to the existing system with storage and can be implemented in a distributed way, where each agent locally responds to price. We analyze the connection between the size of storage, consumer utility function properties and generation fluctuation in two scenarios: when demand is inelastic, we can explicitly characterize the optimal storage access policy and the generation fluctuation; when demand is elastic, the relationship between concavity and generation fluctuation is studied.
Author: Angelia Nedić
Publisher: Springer
ISBN: 3319971425
Category : Business & Economics
Languages : en
Pages : 317
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Book Description
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: Baisravan HomChaudhuri
Publisher:
ISBN:
Category :
Languages : en
Pages : 147
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Book Description
This work is intended towards the development of distributed optimization methods for large-scale networked systems. The advancement in technological fields such as networking, communication and computing has facilitated the development of networks which are massively large-scale in nature. One of the important challenges in these networked systems is the evaluation of the optimal point of operation of the system. The problem is essentially challenging due to the high-dimensionality of the problem, distributed nature of resources, lack of global information and dynamic nature of operation of most of these systems. The inadequacies of the traditional centralized optimization techniques in addressing these issues have prompted the researchers to investigate distributed optimization techniques. This research work focuses on developing techniques to carry out the global optimization in a distributed fashion that explores the fundamental idea of decomposing the overall optimization problem into a number of sub-problems that utilize limited information exchanged over the network. Inspired by price-based mechanisms, the research develops two methods. First, a distributed optimization method consisting of dual decomposition and update of dual variables in the subgradient direction is developed for some different classes of resource allocation problems. Although this method is easy to implement, it has its own drawbacks. To address some of the drawbacks in distributed optimization, in this dissertation, a Newton based distributed interior point optimization method is developed. The proposed approach, which is iterative in nature, focuses on the generation of feasible solutions at each iteration and development of mechanisms that demand lesser communication. The convergence and rate of convergence of both the primal and the dual variables in the system is also analyzed using a benchmark Network Utility Maximization (NUM) problem followed by numerical simulation results. A comparative study between the proposed distributed and centralized method of optimization is also provided. The proposed distributed optimization techniques have been applied to real world systems such as optimal power allocation in Smart Grid and utility maximization in Cloud Computing systems. Both the problems belong to the class of large-scale complex network problems. In the power grids, the challenges are augmented with the nature of the decision variables, coupling effect in the network, the global constraints in the system, uncertain nature of renewable power generators, and the large-scale distributed nature of the problem. In cloud computing, resources such as memory, processing, and bandwidth are needed to be allocated to a large number of users to maximize the users' quality of experience. Finally, the research focuses on the development of a stochastic distributed optimization method for solving problems with multi-modal cost functions. As opposed to the unimodal function optimization, the widely practiced gradient descent methods fail to reach the global optimum solution when multi-modal cost functions are considered. In this dissertation, an effort is be made to develop a stochastic distributed optimization method that exploits noise based solution update to prevent the algorithm from converging into local optimum solutions. The method is applied to the Network Utility Maximization problem with multi-modal cost functions, and is compared with Genetic Algorithm.
Author: Giuseppe Notarstefano
Publisher:
ISBN: 9781680836189
Category : Technology & Engineering
Languages : en
Pages : 148
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Book Description
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.
