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Distributed Optimization In Multi Agent Systems Applications To Distributed Regression
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Book Synopsis Distributed Optimization in Multi-agent Systems: Applications to Distributed Regression by : Sundhar Ram Srinivasan
Download or read book Distributed Optimization in Multi-agent Systems: Applications to Distributed Regression written by Sundhar Ram Srinivasan and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The context for this work is cooperative multi-agent systems (MAS). An agent is an intelligent entity that can measure some aspect of its environment, process information and possibly influence the environment through its action. A cooperative MAS can be defined as a loosely coupled network of agents that interact and cooperate to solve problems that are beyond the individual capabilities or knowledge of each agent. The focus of this thesis is distributed stochastic optimization in multi-agent systems. In distributed optimization, the complete optimization problem is not available at a single location but is distributed among different agents. The distributed optimization problem is additionally stochastic when the information available to each agent is with stochastic errors. Communication constraints, lack of global information about the network topology and the absence of coordinating agents make it infeasible to collect all the information at a single location and then treat it as a centralized optimization problem. Thus, the problem has to be solved using algorithms that are distributed, i.e., different parts of the algorithm are executed at different agents, and local, i.e., each agent uses only information locally available to it and other information it can obtain from its immediate neighbors. In this thesis, we will primarily focus on the specific problem of minimizing a sum of functions over a constraint set, when each component function is known partially (with stochastic errors) to a unique agent. The constraint set is known to all the agents. We propose three distributed and local algorithms, establish asymptotic convergence with diminishing stepsizes and obtain rate of convergence results. Stochastic errors, as we will see, arise naturally when the objective function known to an agent has a random variable with unknown statistics. Additionally, stochastic errors also model communication and quantization errors. The problem is motivated by distributed regression in sensor networks and power control in cellular systems. We also discuss an important extension to the above problem. In the extension, the network goal is to minimize a global function of a sum of component functions over a constraint set. Each component function is known to a unique network agent. The global function and the constraint set are known to all the agents. Unlike the previous problem, this problem is not stochastic. However, the objective function in this problem is more general. We propose an algorithm to solve this problem and establish its convergence.
Book Synopsis Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments by : Minghui Zhu
Download or read book Distributed Optimization-Based Control of Multi-Agent Networks in Complex Environments written by Minghui Zhu and published by Springer. This book was released on 2015-06-11 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a concise and in-depth exposition of specific algorithmic solutions for distributed optimization based control of multi-agent networks and their performance analysis. It synthesizes and analyzes distributed strategies for three collaborative tasks: distributed cooperative optimization, mobile sensor deployment and multi-vehicle formation control. The book integrates miscellaneous ideas and tools from dynamic systems, control theory, graph theory, optimization, game theory and Markov chains to address the particular challenges introduced by such complexities in the environment as topological dynamics, environmental uncertainties, and potential cyber-attack by human adversaries. The book is written for first- or second-year graduate students in a variety of engineering disciplines, including control, robotics, decision-making, optimization and algorithms and with backgrounds in aerospace engineering, computer science, electrical engineering, mechanical engineering and operations research. Researchers in these areas may also find the book useful as a reference.
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 Multi-agent Optimization by : Angelia Nedić
Download or read book Multi-agent Optimization written by Angelia Nedić and published by Springer. This book was released on 2018-11-01 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems by : Tatiana Tatarenko
Download or read book Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems written by Tatiana Tatarenko and published by Springer. This book was released on 2017-09-19 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.
Book Synopsis Distributed Average Tracking in Multi-agent Systems by : Fei Chen
Download or read book Distributed Average Tracking in Multi-agent Systems written by Fei Chen and published by Springer Nature. This book was released on 2020-02-04 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track — using local communication only — the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler–Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization. The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry.
Book Synopsis Distributed Search by Constrained Agents by : Amnon Meisels
Download or read book Distributed Search by Constrained Agents written by Amnon Meisels and published by Springer Science & Business Media. This book was released on 2008 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: The well defined model of distributed constraints satisfaction and optimization (DisCSPs/DisCOPs) can serve as the basis for the design and investigation of distributed search algorithms, of protocols and of negotiations and search. This book presents a comprehensive discussion on the field of distributed constraints, its algorithms and its active research areas. The book introduces distributed constraint satisfaction and optimization problems and describes the underlying model.
Book Synopsis Distributed Constraint Satisfaction by : Makoto Yokoo
Download or read book Distributed Constraint Satisfaction written by Makoto Yokoo and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed Constraint Satisfaction gives an overview of Constraint Satisfaction Problems (CSPs), adapts related search algorithms and consistency algorithms for applications to multi-agent systems, and consolidates recent research devoted to cooperation in such systems. The techniques introduced are applied to various problems in multi-agent systems. Among the new approaches is a hybrid-type algorithm for weak-commitment search combining backtracking and iterative improvement. Also, an extension of the basic CSP formalization called "Partial CSP" is introduced in order to handle over-constrained CSPs.
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 in Multi-agent Systems by : Salar Rahili
Download or read book Distributed Optimization in Multi-agent Systems written by Salar Rahili and published by . This book was released on 2016 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: In last part of this dissertation, the distributed average tracking problem is addressed for a group of heterogeneous physical agents consisting of single-integrator, double-integrator and Euler-Lagrange dynamics. Here, the goal is that each agent uses local information and local interaction to calculate the average of individual time-varying reference inputs, one per agent. Dynamic average tracking is the main challenge in many other distributed algorithms, such as distributed optimization, and distributed Kalman filtering.
Book Synopsis Distributed Constraint Problem Solving and Reasoning in Multi-agent Systems by : Weixiong Zhang
Download or read book Distributed Constraint Problem Solving and Reasoning in Multi-agent Systems written by Weixiong Zhang and published by IOS Press. This book was released on 2004 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed and multi-agent systems are becoming more and more the focus of attention in artificial intelligence research and have already found their way into many practical applications. An important prerequisite for their success is an ability to flexibly adapt their behavior via intelligent cooperation. Successful reasoning about and within a multiagent system is therefore paramount to achieve intelligent behavior. Distributed Constraint Satisfaction Problems (DCSPs) and Distributed Constraint Optimization (minimization) Problems (DCOPs) are perhaps ubiquitous in distributed systems in dynamic environments. Many important problems in distributed environments and systems, such as action coordination, task scheduling and resource allocation, can be formulated and solved as DCSPs and DCOPs. Therefore, techniques for solving DCSPs and DCOPs as well as strategies for automated reasoning in distributed systems are indispensable tools in the research areas of distributed and multi-agent systems. They also provide promising frameworks to deal with the increasingly diverse range of distributed real world problems emerging from the fast evolution of communication technologies.The volume is divided in two parts. One part contains papers on distributed constraint problems in multi-agent systems. The other part presents papers on Agents and Automated Reasoning.
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 Probability Collectives by : Anand Jayant Kulkarni
Download or read book Probability Collectives written by Anand Jayant Kulkarni and published by Springer. This book was released on 2015-02-25 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
Book Synopsis Distributed Algorithms for Networked Multi-agent Systems: Optimization and Competition by :
Download or read book Distributed Algorithms for Networked Multi-agent Systems: Optimization and Competition written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Intelligent Distributed Computing XII by : Javier Del Ser
Download or read book Intelligent Distributed Computing XII written by Javier Del Ser and published by Springer. This book was released on 2018-09-14 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a wealth of research contributions on recent advances in intelligent and distributed computing, and which present both architectural and algorithmic findings in these fields. A major focus is placed on new techniques and applications for evolutionary computation, swarm intelligence, multi-agent systems, multi-criteria optimization and Deep/Shallow machine learning models, all of which are approached as technological drivers to enable autonomous reasoning and decision-making in complex distributed environments. Part of the book is also devoted to new scheduling and resource allocation methods for distributed computing systems. The book represents the peer-reviewed proceedings of the 12th International Symposium on Intelligent Distributed Computing (IDC 2018), which was held in Bilbao, Spain, from October 15 to 17, 2018.
