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Network-System Research and Distributed Composite Algorithm Design: Computational Intelligence Methods and Applications

Autor Huaqing Li, Qingguo Lü, Dawen Xia, Xin Wang, Zheng Wang, Lifeng Zheng, Jun Li, Liang Ran
en Limba Engleză Hardback – 25 noi 2025

În volumul Network-System Research and Distributed Composite Algorithm Design, echipa de autori condusă de Huaqing Li și Qingguo Lü își valorifică expertiza vastă în teoria optimizării pentru a răspunde nevoilor actuale de procesare din ecosistemele digitale masive. Observăm o orientare clară către rezolvarea limitărilor metodelor centralizate tradiționale, autorii propunând soluții adaptate pentru noduri de calcul multiple care colaborează în rețea. Această abordare este esențială în contextul actual, unde economia la altitudine joasă și modelele de limbaj de mari dimensiuni (LLM) solicită o infrastructură algoritmică robustă. Suntem de părere că forța acestui text rezidă în rigoarea cu care sunt tratate mecanismele de comunicare asincronă și tehnicile de coordonate de bloc aleatorii. Autorii nu se limitează la teorie, ci integrează natural scheme de divizare a operatorilor și metode punct fix, oferind un cadru tehnic pentru optimizarea compozită și multi-bloc. Abordarea diferă de Algorithm Design for Networked Information Technology Systems de Sumit Ghosh prin faptul că este mai puțin abstractă și mult mai aplicabilă, concentrându-se pe implementări specifice pentru [smart grids] și [model predictive control]. Dacă lucrarea lui Ghosh explorează perspective transdisciplinare, volumul de față este un instrument de lucru precis pentru ingineria sistemelor complexe. Structura este una progresivă, de la protocoale de consens la strategii de pași necoordonați, menținând un ritm tehnic susținut. Comparativ cu Distributed Optimization in Networked Systems, tot sub semnătura lui Qingguo Lü, acest nou volum extinde cercetarea către optimizarea compozită, oferind soluții pentru funcții obiectiv cu diversitate ridicată, devenind astfel o resursă tehnică de referință în portofoliul editurii Springer.

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Specificații

ISBN-13: 9789819509102
ISBN-10: 9819509106
Pagini: 256
Ilustrații: XIII, 241 p. 1 illus.
Dimensiuni: 160 x 241 x 20 mm
Greutate: 0.55 kg
Editura: Springer
Colecția Computational Intelligence Methods and Applications
Seria Computational Intelligence Methods and Applications


De ce să citești această carte

Recomandăm această carte inginerilor și cercetătorilor care dezvoltă sisteme distribuite complexe. Cititorul câștigă acces la algoritmi de ultimă generație pentru optimizarea multi-bloc și asincronă, esențiali în AI și rețele inteligente. Este un ghid tehnic riguros care transformă matematica avansată în soluții scalabile pentru provocările reale de calcul din prezent.


Descriere

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 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.

Notă biografică

Xin Wang received his Ph.D. degree in Computer Science and Technology from Chongqing University, Chongqing, China, 2015. 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, since 2023. He has published about more than 50 journal papers.

Huaqing Li (senior member, IEEE) received the BS degree in information and computing science from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2009, and the Ph.D. degree in computer science and technology from Chongqing University, Chongqing, in 2013. He is currently a professor with the College of Electronic and Information Engineering, Southwest University, Chongqing. His main research interests include nonlinear dynamics and control, multiagent systems, and distributed optimization. He serves as a regional editor for Neural Computing and Applications and an Editorial Board Member for IEEE ACCESS.

Tingwen Huang is a professor at Texas A&M University at Qatar. He received his Ph.D. degree from Texas A&M University, College Station, Texas, 2002. After graduated from Texas A&M University, he worked as a visiting assistant professor there. Then he joined Texas A\& M University at Qatar (TAMUQ) as an Assistant Professor in August 2003, and then he was promoted to a professor in 2013. Dr. Huang's focus areas for research interests include neural networks, chaotic dynamical systems, complex networks, optimization, and control. He has authored and co-authored more than 100 refereed journal papers.