
Multi-agent Optimization
Cetraro, Italy 2014
Springer (Publisher)
Published on 2. November 2018
Book
Paperback/Softback
VII, 308 pages
978-3-319-97141-4 (ISBN)
Description
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.
More details
Product info
Book
Series
Edition
1st ed. 2018
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
College/higher education
Professional and scholarly
Illustrations
22 farbige Tabellen, 6 s/w Abbildungen, 22 farbige Abbildungen
4 schwarz-weiße und 22 farbige Abbildungen, 22 farbige Tabellen, Bibliographie
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 18 mm
Weight
487 gr
ISBN-13
978-3-319-97141-4 (9783319971414)
DOI
10.1007/978-3-319-97142-1
Schweitzer Classification
Other editions
Additional editions

E-Book
11/2018
Springer
€69.54
Available for download
Content
Preface.- Distributed Optimization over Networks by Angelia Nedich. - Five Lectures on Differential Variational Inequalities by Jong-Shi Pang. - Parallel and Distributed Successive Convex Approximation Methods for Big-Data Optimization by Gesualdo Scutari and Ying Sun.