Big Data Optimization: Recent Developments and Challenges edited by Ali Emrouznejad.

The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners i...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Emrouznejad, Ali (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:Studies in Big Data, 18
Springer eBook Collection.
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Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary:The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Physical Description:XV, 487 p. 182 illus., 160 illus. in color. online resource.
ISBN:9783319302652
ISSN:2197-6503 ;
DOI:10.1007/978-3-319-30265-2