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[null,"1.","Record Nr.",null,"UNINA9911047717303321"]
[null,null,"Autore",null,"Li Huaqing"]
[null,null,"Titolo",null,"Network-System Research and Distributed Composite Algorithm Design / / by Huaqing Li, Qingguo Lü, Dawen Xia, Xin Wang, Zheng Wang, Lifeng Zheng, Jun Li, Liang Ran"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2026"]
[null,null,"ISBN",null,"981-9509-11-4"]
[null,null,"Edizione",null,"[1st ed. 2026.]"]
[null,null,"Descrizione fisica",null,"1 online resource (452 pages)"]
[null,null,"Collana",null,"Computational Intelligence Methods and Applications, , 2510-1773"]
[null,null,"Disciplina",null,"005.13"]
[null,null,"Soggetti",null,"Algorithms"]
[{"$nulls":4},"Computer science"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Design and Analysis of Algorithms"]
[{"$nulls":4},"Theory and Algorithms for Application Domains"]
[{"$nulls":4},"Computer Engineering and Networks"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"\"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\"."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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 protocols, 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."]