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Distributed Optimization With Applications To Sensor Networks And Machine Learning
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Book Synopsis Distributed Optimization with Application to Power Systems and Control by : Engelmann, Alexander
Download or read book Distributed Optimization with Application to Power Systems and Control written by Engelmann, Alexander and published by KIT Scientific Publishing. This book was released on 2022-11-21 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization.
Book Synopsis Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers by : Stephen Boyd
Download or read book Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers written by Stephen Boyd and published by Now Publishers Inc. This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others.
Book Synopsis Intelligent Sensor Networks by : Fei Hu
Download or read book Intelligent Sensor Networks written by Fei Hu and published by CRC Press. This book was released on 2012-12-15 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although governments worldwide have invested significantly in intelligent sensor network research and applications, few books cover intelligent sensor networks from a machine learning and signal processing perspective. Filling this void, Intelligent Sensor Networks: The Integration of Sensor Networks, Signal Processing and Machine Learning focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on the world-class research of award-winning authors, the book provides a firm grounding in the fundamentals of intelligent sensor networks, including compressive sensing and sampling, distributed signal processing, and intelligent signal learning. Presenting recent research results of world-renowned sensing experts, the book is organized into three parts: Machine Learning—describes the application of machine learning and other AI principles in sensor network intelligence—covering smart sensor/transducer architecture and data representation for intelligent sensors Signal Processing—considers the optimization of sensor network performance based on digital signal processing techniques—including cross-layer integration of routing and application-specific signal processing as well as on-board image processing in wireless multimedia sensor networks for intelligent transportation systems Networking—focuses on network protocol design in order to achieve an intelligent sensor networking—covering energy-efficient opportunistic routing protocols for sensor networking and multi-agent-driven wireless sensor cooperation Maintaining a focus on "intelligent" designs, the book details signal processing principles in sensor networks. It elaborates on critical platforms for intelligent sensor networks and illustrates key applications—including target tracking, object identification, and structural health monitoring. It also includes a paradigm for validating the extent of spatiotemporal associations among data sources to enhance data cleaning in sensor networks, a sensor stream reduction application, and also considers the use of Kalman filters for attack detection in a water system sensor network that consists of water level sensors and velocity sensors.
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 2024-07-18 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 Multiscale Optimization Methods and Applications by : William W. Hager
Download or read book Multiscale Optimization Methods and Applications written by William W. Hager and published by Springer Science & Business Media. This book was released on 2006-06-18 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: As optimization researchers tackle larger and larger problems, scale interactions play an increasingly important role. One general strategy for dealing with a large or difficult problem is to partition it into smaller ones, which are hopefully much easier to solve, and then work backwards towards the solution of original problem, using a solution from a previous level as a starting guess at the next level. This volume contains 22 chapters highlighting some recent research. The topics of the chapters selected for this volume are focused on the development of new solution methodologies, including general multilevel solution techniques, for tackling difficult, large-scale optimization problems that arise in science and industry. Applications presented in the book include but are not limited to the circuit placement problem in VLSI design, a wireless sensor location problem, optimal dosages in the treatment of cancer by radiation therapy, and facility location.
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 for Smart Cyber-Physical Networks by : Giuseppe Notarstefano
Download or read book Distributed Optimization for Smart Cyber-Physical Networks written by Giuseppe Notarstefano and published by . This book was released on 2019-12-11 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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 Parallel and Distributed Computing, Applications and Technologies by : Yong Zhang
Download or read book Parallel and Distributed Computing, Applications and Technologies written by Yong Zhang and published by Springer Nature. This book was released on 2021-02-20 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020, which took place in Shenzhen, China, during December 28-30, 2020. The 34 full papers included in this volume were carefully reviewed and selected from 109 submissions. They deal with parallel and distributed computing of networking and architectures, software systems and technologies, algorithms and applications, and security and privacy.
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 Advances in Optimization and Applications by : Nicholas Olenev
Download or read book Advances in Optimization and Applications written by Nicholas Olenev and published by Springer Nature. This book was released on 2023-01-01 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 13th International Conference on Advances in Optimization and Applications, OPTIMA 2022, held in Petrovac, Montenegro, during September 26–30, 2022. The 13 full papers included in this book were carefully reviewed and selected from 26 submissions. They were organized in topical sections as follows: mathematical programming; global optimization; discrete and combinatorial optimization; optimization and data analysis; game theory and mathematical economics; and applications.
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 Mathematical Optimization Theory and Operations Research by : Anton Eremeev
Download or read book Mathematical Optimization Theory and Operations Research written by Anton Eremeev and published by Springer Nature. This book was released on with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control by : Zhang Ren
Download or read book Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control written by Zhang Ren and published by Springer Nature. This book was released on 2022-07-29 with total page 1902 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original, peer-reviewed research papers from the 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control (CCSICC2021), held in Shenzhen, China on January 19-22, 2022. The topics covered include but are not limited to: reviews and discussions of swarm intelligence, basic theories on swarm intelligence, swarm communication and networking, swarm perception, awareness and location, swarm decision and planning, cooperative control, cooperative guidance, swarm simulation and assessment. The papers showcased here share the latest findings on theories, algorithms and applications in swarm intelligence and cooperative control, making the book a valuable asset for researchers, engineers, and university students alike.
Book Synopsis Optimization for Machine Learning by : Suvrit Sra
Download or read book Optimization for Machine Learning written by Suvrit Sra and published by MIT Press. This book was released on 2012 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Book Synopsis Encyclopedia of Systems and Control by : John Baillieul
Download or read book Encyclopedia of Systems and Control written by John Baillieul and published by Springer. This book was released on 2015-07-29 with total page 1554 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Systems and Control collects a broad range of short expository articles that describe the current state of the art in the central topics of control and systems engineering as well as in many of the related fields in which control is an enabling technology. The editors have assembled the most comprehensive reference possible, and this has been greatly facilitated by the publisher’s commitment continuously to publish updates to the articles as they become available in the future. Although control engineering is now a mature discipline, it remains an area in which there is a great deal of research activity, and as new developments in both theory and applications become available, they will be included in the online version of the encyclopedia. A carefully chosen team of leading authorities in the field has written the well over 250 articles that comprise the work. The topics range from basic principles of feedback in servomechanisms to advanced topics such as the control of Boolean networks and evolutionary game theory. Because the content has been selected to reflect both foundational importance as well as subjects that are of current interest to the research and practitioner communities, a broad readership that includes students, application engineers, and research scientists will find material that is of interest.
Book Synopsis Advanced Data Mining and Applications by : Longbing Cao
Download or read book Advanced Data Mining and Applications written by Longbing Cao and published by Springer Science & Business Media. This book was released on 2010-11-05 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th International Conference on Advanced Data Mining and Applications, ADMA 2010, held in Chongqing, China, in November 2010. 63 carefully reviewed regular papers and 55 revised short papers were presented. The papers are organized in topical sections on data mining foundations; data mining in specific areas; data mining methodologies and processes; and data mining applications and systems.
