Network-System Research and Distributed Composite Algorithm Design
With the rapid advancement of sensor technology and digital system, the capabilities of network communication have significantly improved, allowing multiple computing nodes to exchange information and collaborate seamlessly through networks. This progress has accelerated the development of distributed optimization theory and its applications in emerging fields such as low altitude economy, big data, and artificial intelligence. These emerging domains usually involve solving complex large-scale optimization problems, making it difficult for traditional centralized methods to handle. Therefore, it is necessary to study distributed algorithms to solve complex optimization problems in large-scale networked systems. In addition, the emergence of applications of large language model further stimulates researchers’ growing interest in distributed optimization. This book provides the advanced methods and techniques of distributed optimization in networked systems, and thus is necessary and important for the research community.

This book focuses on designing high-performance algorithms for solving more practical and complex optimization problems (multi-block optimization, composite optimization, constrained optimization, optimization with diversity objective functions, etc.) in the context of distributed optimization in networked systems and their successful application to real-world applications (model predictive control, smart grids, etc.). Readers may be particularly interested in the book on consensus and optimization prools, forward-backward splitting methods, proximal gradient methods, primal-dual methods, fixed point methods, asynchronous communication/computaion mechanisms, randomized block coordinate techniques, operator splitting schemes, uncoordinated step sizes strategies, etc., in the process of distributed optimization in various networked systems.

This book will introduce readers to the latest and advanced techniques in “Network-System Research and Distributed Composite Algorithm Design”, and help them develop their own novel distributed algorithms that have practical applications. The prerequisite for understanding this book is to master basic mathematical knowledge, including graph theory, matrix theory, linear algebra, probability theory, etc. This book is meant for the researcher and engineer who uses distributed optimization algorithms in fields like control theory, electronic information, artificial intelligence, and computer science, etc. It can also serve as complementary reading for distributed optimization in networked systems at the post-graduate level.

1147795990
Network-System Research and Distributed Composite Algorithm Design
With the rapid advancement of sensor technology and digital system, the capabilities of network communication have significantly improved, allowing multiple computing nodes to exchange information and collaborate seamlessly through networks. This progress has accelerated the development of distributed optimization theory and its applications in emerging fields such as low altitude economy, big data, and artificial intelligence. These emerging domains usually involve solving complex large-scale optimization problems, making it difficult for traditional centralized methods to handle. Therefore, it is necessary to study distributed algorithms to solve complex optimization problems in large-scale networked systems. In addition, the emergence of applications of large language model further stimulates researchers’ growing interest in distributed optimization. This book provides the advanced methods and techniques of distributed optimization in networked systems, and thus is necessary and important for the research community.

This book focuses on designing high-performance algorithms for solving more practical and complex optimization problems (multi-block optimization, composite optimization, constrained optimization, optimization with diversity objective functions, etc.) in the context of distributed optimization in networked systems and their successful application to real-world applications (model predictive control, smart grids, etc.). Readers may be particularly interested in the book on consensus and optimization prools, forward-backward splitting methods, proximal gradient methods, primal-dual methods, fixed point methods, asynchronous communication/computaion mechanisms, randomized block coordinate techniques, operator splitting schemes, uncoordinated step sizes strategies, etc., in the process of distributed optimization in various networked systems.

This book will introduce readers to the latest and advanced techniques in “Network-System Research and Distributed Composite Algorithm Design”, and help them develop their own novel distributed algorithms that have practical applications. The prerequisite for understanding this book is to master basic mathematical knowledge, including graph theory, matrix theory, linear algebra, probability theory, etc. This book is meant for the researcher and engineer who uses distributed optimization algorithms in fields like control theory, electronic information, artificial intelligence, and computer science, etc. It can also serve as complementary reading for distributed optimization in networked systems at the post-graduate level.

199.99 Pre Order
Network-System Research and Distributed Composite Algorithm Design

Network-System Research and Distributed Composite Algorithm Design

Network-System Research and Distributed Composite Algorithm Design

Network-System Research and Distributed Composite Algorithm Design

Overview

With the rapid advancement of sensor technology and digital system, the capabilities of network communication have significantly improved, allowing multiple computing nodes to exchange information and collaborate seamlessly through networks. This progress has accelerated the development of distributed optimization theory and its applications in emerging fields such as low altitude economy, big data, and artificial intelligence. These emerging domains usually involve solving complex large-scale optimization problems, making it difficult for traditional centralized methods to handle. Therefore, it is necessary to study distributed algorithms to solve complex optimization problems in large-scale networked systems. In addition, the emergence of applications of large language model further stimulates researchers’ growing interest in distributed optimization. This book provides the advanced methods and techniques of distributed optimization in networked systems, and thus is necessary and important for the research community.

This book focuses on designing high-performance algorithms for solving more practical and complex optimization problems (multi-block optimization, composite optimization, constrained optimization, optimization with diversity objective functions, etc.) in the context of distributed optimization in networked systems and their successful application to real-world applications (model predictive control, smart grids, etc.). Readers may be particularly interested in the book on consensus and optimization prools, forward-backward splitting methods, proximal gradient methods, primal-dual methods, fixed point methods, asynchronous communication/computaion mechanisms, randomized block coordinate techniques, operator splitting schemes, uncoordinated step sizes strategies, etc., in the process of distributed optimization in various networked systems.

This book will introduce readers to the latest and advanced techniques in “Network-System Research and Distributed Composite Algorithm Design”, and help them develop their own novel distributed algorithms that have practical applications. The prerequisite for understanding this book is to master basic mathematical knowledge, including graph theory, matrix theory, linear algebra, probability theory, etc. This book is meant for the researcher and engineer who uses distributed optimization algorithms in fields like control theory, electronic information, artificial intelligence, and computer science, etc. It can also serve as complementary reading for distributed optimization in networked systems at the post-graduate level.

Product Details

ISBN-13: 9789819509102
Publisher: Springer Nature Singapore
Publication date: 10/25/2025
Series: Computational Intelligence Methods and Applications
Pages: 245
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

Huaqing Li currently a Professor with the College of Electronic and Information Engineering, Southwest University, Chongqing, China. His main research interests include nonlinear dynamics and control, multi-agent systems, and distributed optimization. He has authored or coauthored more than 100 related to the proposed book’s topic. He currently serves as an Editorial Board Member for the IEEE Transactions on Industrial Cyber-Physical Systems, IEEE Transactions on Systems, Man and Cybernetics: Systems, Neural Computing and Applications, Frontiers of Information Technology & Electronic Engineering, etc.

Qingguo Lü was a recipient of the Outstanding Ph.D. Thesis Award of ACM China (Chongqing Branch) in 2021, the Outstanding Master’s Thesis Award of Chongqing in 2019, and the Best Poster Award of EEI 2024. He is currently an Associate Researcher at College of Computer Science, Chongqing University, Chongqing, China.

Dawen Xia is currently a Professor at the College of Microelectronics and Artificial Intelligence & College of Big Data Engineering & Engineering Research Center of Micro-nano and Intelligent Manufacturing, Ministry of Education, Kaili University, Kaili, China and the College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China. His research interests include distributed optimization, big data analytics, artificial intelligence, and data mining.

Xin Wang, from 2018 to 2019, he was a Visiting Scholar with the Humboldt University of Berlin, Berlin, Germany, and with the Potsdam Institute for Climate Impact Research, Potsdam, Germany. Since 2018, he has been a Professor with the School of Electronic and Information Engineering, Southwest University, Chongqing, China.

Zheng Wang is currently a Lecture with the College of Electronic and Information Engineering, Southwest University, China. His research interests include multiagent systems, distributed optimization, game, and their applications in smart grids.

Lifeng Zheng is currently a lecturer with the College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China. His research interests include multi-agent systems, model predictive control, game theory, and distributed optimization.

Jun Li is currently working toward the PhD degree with the College of Electronic and Information Engineering, Southwest University, Chongqing, China. His research interests include multiagent systems, distributed optimization, and reinforcement learning.

Liang Ran is currently working toward the Ph.D. degree in computer science and technology. His research interests include game theory, cooperative control, multiagent systems, and distributed optimization.

Table of Contents

"1. Distributed Primal-Dual Splitting Algorithm for Multi-Block Separable Optimization".- "2. Distributed Primal-Dual Fixed Point Algorithm for Multi-Block Separable Optimization".- "3. Distributed Triple Proximal Splitting Algorithm for Nonsmooth Composite Optimization".- "4. Distributed Dual Operator Splitting Algorithm for Nonsmooth Composite Optimization".- "5. Distributed Primal-Dual Forward-Backward Splitting Algorithm for Nonsmooth Composite Optimization".- "6. Distributed Algorithm for Constrained Optimization with Asynchrony and Delays".- "7. Distributed Model Predictive Control Algorithm for Optimal Output Consensus of High-Order Multi-Agent Systems"- "8. Distributed Dual Proximal Gradient Algorithms for Nonsmooth Constrained Composite Optimization".- "9. Distributed Proximal Splitting Algorithm for Composite Constrained Convex Optimization".

From the B&N Reads Blog
Page 1 of

Customer Reviews