Distributed Optimization And Learning

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Distributed Optimization And Learning

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Author : Zhongguo Li
language : en
Publisher: Elsevier
Release Date : 2024-07-18

Distributed Optimization And Learning written by Zhongguo Li and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-18 with Technology & Engineering categories.

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

Distributed Optimization And Statistical Learning Via The Alternating Direction Method Of Multipliers

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Author : Stephen Boyd
language : en
Publisher: Now Publishers Inc
Release Date : 2011

Distributed Optimization And Statistical Learning Via The Alternating Direction Method Of Multipliers written by Stephen Boyd and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.

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.

Distributed Optimization Game And Learning Algorithms

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Author : Huiwei Wang
language : en
Publisher: Springer Nature
Release Date : 2021-01-04

Distributed Optimization Game And Learning Algorithms written by Huiwei Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-04 with Technology & Engineering categories.

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.

Distributed Optimization Advances In Theories Methods And Applications

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Author : Huaqing Li
language : en
Publisher: Springer Nature
Release Date : 2020-08-04

Distributed Optimization Advances In Theories Methods And Applications written by Huaqing Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-04 with Technology & Engineering categories.

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.

Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems

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Author : Tatiana Tatarenko
language : en
Publisher: Springer
Release Date : 2017-09-19

Game Theoretic Learning And Distributed Optimization In Memoryless Multi Agent Systems written by Tatiana Tatarenko and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-19 with Science categories.

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.

Policy Advice Non Convex And Distributed Optimization In Reinforcement Learning

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Author : Yusen Zhan
language : en
Publisher:
Release Date : 2016

Policy Advice Non Convex And Distributed Optimization In Reinforcement Learning written by Yusen Zhan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Abstract not available.

Optimization Algorithms For Distributed Machine Learning

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Author : Gauri Joshi
language : en
Publisher: Springer Nature
Release Date : 2022-11-25

Optimization Algorithms For Distributed Machine Learning written by Gauri Joshi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-25 with Computers categories.

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.

Distributed Optimization With Applications To Sensor Networks And Machine Learning

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Author :
language : en
Publisher:
Release Date : 2012

Distributed Optimization With Applications To Sensor Networks And Machine Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

Large Scale And Distributed Optimization

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Author : Pontus Giselsson
language : en
Publisher: Springer
Release Date : 2018-11-11

Large Scale And Distributed Optimization written by Pontus Giselsson and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-11 with Mathematics categories.

This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.

Optimization Learning And Control For Interdependent Complex Networks

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Author : M. Hadi Amini
language : en
Publisher: Springer Nature
Release Date : 2020-02-22

Optimization Learning And Control For Interdependent Complex Networks written by M. Hadi Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-22 with Technology & Engineering categories.

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role in smart cities operation, smart energy systems, and intelligent transportation networks. It paves the way for researchers working on optimization, learning, and control spread over the fields of computer science, operation research, electrical engineering, civil engineering, and system engineering. This book also covers optimization algorithms for large-scale problems from theoretical foundations to real-world applications, learning-based methods to enable intelligence in smart cities, and control techniques to deal with the optimal and robust operation of complex systems. It further introduces novel algorithms for data analytics in large-scale interdependent complex networks. • Specifies the importance of efficient theoretical optimization and learning methods in dealing with emerging problems in the context of interdependent networks • Provides a comprehensive investigation of advance data analytics and machine learning algorithms for large-scale complex networks • Presents basics and mathematical foundations needed to enable efficient decision making and intelligence in interdependent complex networks M. Hadi Amini is an Assistant Professor at the School of Computing and Information Sciences at Florida International University (FIU). He is also the founding director of Sustainability, Optimization, and Learning for InterDependent networks laboratory (solid lab). He received his Ph.D. and M.Sc. from Carnegie Mellon University in 2019 and 2015 respectively. He also holds a doctoral degree in Computer Science and Technology. Prior to that, he received M.Sc. from Tarbiat Modares University in 2013, and the B.Sc. from Sharif University of Technology in 2011.