Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
An Integrated Algorithm For Distributed Optimization In Networked Systems
Download An Integrated Algorithm For Distributed Optimization In Networked Systems full books in PDF, epub, and Kindle. Read online An Integrated Algorithm For Distributed Optimization In Networked Systems ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis An Integrated Algorithm for Distributed Optimization in Networked Systems by : Yapeng Lu
Download or read book An Integrated Algorithm for Distributed Optimization in Networked Systems written by Yapeng Lu and published by Open Dissertation Press. This book was released on 2017-01-27 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis An Integrated Algorithm for Distributed Optimization in Networked Systems by : Yapeng Lu
Download or read book An Integrated Algorithm for Distributed Optimization in Networked Systems written by Yapeng Lu and published by . This book was released on 2009 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distributed Optimization in Networked Systems by : Qingguo Lü
Download or read book Distributed Optimization in Networked Systems written by Qingguo Lü and published by Springer Nature. This book was released on 2023-02-08 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Distributed Optimization: Advances in Theories, Methods, and Applications by : Huaqing Li
Download or read book Distributed Optimization: Advances in Theories, Methods, and Applications written by Huaqing Li and published by Springer Nature. This book was released on 2020-08-04 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Distributed Optimization and Learning by : Zhongguo Li
Download or read book Distributed Optimization and Learning written by Zhongguo Li and published by Elsevier. This book was released on 2024-08-06 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Distributed Consensus, Optimization and Computation in Networked Systems by : Lisha Yao
Download or read book Distributed Consensus, Optimization and Computation in Networked Systems written by Lisha Yao and published by . This book was released on 2018 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first part of this thesis, we propose a distributed consensus algorithm under multi-layer multi-group structure with communication time delays. It is proven that the consensus will be achieved in both time-varying and fixed communication delays. In the second part, we study the distributed optimization problem with a finite-time mechanism. It is shown that our distributed proportional-integral algorithm can exponentially converge to the unique global minimizer when the gain parameters satisfy the sufficient conditions. Moreover, we equip the proposed algorithm with a decentralized algorithm, which enables an arbitrarily chosen agent to compute the exact global minimizer within a finite number of time steps, using its own states observed over a successive time steps. In the third part, it is shown the implementation of accelerated distributed energy management for microgrids is achieved. The results presented in the thesis are corroborated by simulations or experiments.
Book Synopsis Design of Distributed and Robust Optimization Algorithms. A Systems Theoretic Approach by : Simon Michalowsky
Download or read book Design of Distributed and Robust Optimization Algorithms. A Systems Theoretic Approach written by Simon Michalowsky and published by Logos Verlag Berlin GmbH. This book was released on 2020-04-17 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization algorithms are the backbone of many modern technologies. In this thesis, we address the analysis and design of optimization algorithms from a systems theoretic viewpoint. By properly recasting the algorithm design as a controller synthesis problem, we derive methods that enable a systematic design of tailored optimization algorithms. We consider two specific classes of optimization algorithms: (i) distributed, and (ii) robust optimization algorithms. Concerning (i), we utilize ideas from geometric control in an innovative fashion to derive a novel methodology that enables the design of distributed optimization algorithms under minimal assumptions on the graph topology and the structure of the optimization problem. Concerning (ii), we employ robust control techniques to establish a framework for the analysis of existing algorithms as well as the design of novel robust optimization algorithms with specified guarantees.
Book Synopsis Optimization Problems in Self-Organizing Networks by : Steffen Wolf
Download or read book Optimization Problems in Self-Organizing Networks written by Steffen Wolf and published by Logos Verlag Berlin GmbH. This book was released on 2010 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern computer networks or wireless ad-hoc networks offer a wide range of interesting optimization problems. Usual optimization goals are the minimization of the message delay in a Peer-to-Peer system or the minimization of the energy consumption of a wireless network. This thesis presents different kinds of algorithms to solve such optimization problems. Starting from the mathematical formulations for these problems, various global view optimization algorithms are presented. These algorithms are based on evolutionary algorithms and local search or similar heuristics. They can be used to quickly find near-optimal solutions, if a global view of the network is possible. As the participants in a computer network or a wireless ad-hoc network are autonomous nodes, distributed algorithms can be designed that enable these nodes to collectively solve the optimization problem. Four distributed algorithms are formulated and evaluated in this thesis, thus laying grounds for distributed optimization of networks. Using these algorithms, the network can be modelled as a self-optimizing network and the optimization problem can be approached without global view.
Book Synopsis Network Optimization: Continuous and Discrete Models by : Dimitri Bertsekas
Download or read book Network Optimization: Continuous and Discrete Models written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 1998-01-01 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: An insightful, comprehensive, and up-to-date treatment of linear, nonlinear, and discrete/combinatorial network optimization problems, their applications, and their analytical and algorithmic methodology. It covers extensively theory, algorithms, and applications, and it aims to bridge the gap between linear and nonlinear network optimization on one hand, and integer/combinatorial network optimization on the other. It complements several of our books: Convex Optimization Theory (Athena Scientific, 2009), Convex Optimization Algorithms (Athena Scientific, 2015), Introduction to Linear Optimization (Athena Scientific, 1997), Nonlinear Programming (Athena Scientific, 1999), as well as our other book on the subject of network optimization Network Flows and Monotropic Optimization (Athena Scientific, 1998).
Book Synopsis Price-based Distributed Optimization in Large-scale Networked Systems by : Baisravan HomChaudhuri
Download or read book Price-based Distributed Optimization in Large-scale Networked Systems written by Baisravan HomChaudhuri and published by . This book was released on 2013 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Distributed Coding and Algorithm Optimization for Large-scale Networked Systems by : Saber Jafarizadeh
Download or read book Distributed Coding and Algorithm Optimization for Large-scale Networked Systems written by Saber Jafarizadeh and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distributed Optimization, Game and Learning Algorithms by : Huiwei Wang
Download or read book Distributed Optimization, Game and Learning Algorithms written by Huiwei Wang and published by Springer Nature. This book was released on 2021-01-04 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Network-based Distributed Planning Using Coevolutionary Algorithms by : Arthur C Sanderson
Download or read book Network-based Distributed Planning Using Coevolutionary Algorithms written by Arthur C Sanderson and published by World Scientific. This book was released on 2004-02-26 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, efficient and scalable coevolutionary algorithms for distributed, network-based decision-making, which utilize objective functions are developed in a networked environment where internode communications are a primary factor in system performance.A theoretical foundation for this class of coevolutionary algorithms is introduced using techniques from stochastic process theory and mathematical analysis.A case study in distributed, network-based decision-making presents an implementation and detailed evaluation of the coevolutionary decision-making framework that incorporates distributed evolutionary agents and mobile agents.The methodology discussed in this book can have a fundamental impact on the principles and practice of engineering in the distributed, network-based environment that is emerging within and among corporate enterprise systems. In addition, the conceptual framework of the approach to distributed decision systems described may have much wider implications for network-based systems and applications.
Book Synopsis The Role of the Network in Distributed Optimization Algorithms by : Konstantinos Tsianos
Download or read book The Role of the Network in Distributed Optimization Algorithms written by Konstantinos Tsianos and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The contributions of this work can be grouped into four important areas: 1) understanding the communication/computation tradeoff and its effect on scalability with the network size, 2) understanding the limitations of the network and the necessary features that distributed algorithms need to possess to be practical, 3) understanding the effects on convergence of network-induced communication delays and 4) understanding the theoretically achievable convergence rates of distributed algorithms. These areas impact the design and deployment of any consensus-based distributed optimization algorithm. " --
Book Synopsis On Distributed Optimization in Networked Systems by : Björn Johansson
Download or read book On Distributed Optimization in Networked Systems written by Björn Johansson and published by . This book was released on 2008 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distributed Optimization with Limited Communication in Networks with Adversaries by : Iyanuoluwa Emiola
Download or read book Distributed Optimization with Limited Communication in Networks with Adversaries written by Iyanuoluwa Emiola and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We all hope for the best but sometimes, one must plan for ways of dealing with the worst-case scenarios, especially in a network with adversaries. This dissertation illustrates a detailed description of distributed optimization algorithms over a network of agents, in which some agents are adversarial. The model considered is such that adversarial agents act to subvert the objective of the network. The algorithms presented in this dissertation are solved via gradient-based distributed optimization algorithm and the effects of the adversarial agents on the convergence of the algorithm to the optimal solution are characterized. The analyses presented establish conditions under which the adversarial agents have enough information to obstruct convergence to the optimal solution by the non-adversarial agents. The adversarial agents act by using up network bandwidth, forcing the communication of the non-adversarial agents to be constrained. A distributed gradient-based optimization algorithm is explored in which the non-adversarial agents exchange quantized information with one another using fixed and adaptive quantization scheme. Additionally, convergence of the solution to a neighborhood of the optimal solution is proved in the communication-constrained environment amidst the presence of adversarial agents.
Download or read book NBS Special Publication written by and published by . This book was released on 1978 with total page 790 pages. Available in PDF, EPUB and Kindle. Book excerpt:
