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Big Data Optimization: Recent Developments and Challenges (Studies in Big Data, nr. 18)

Editat de Ali Emrouznejad
Notă GoodReads:
en Limba Engleză Hardback – 07 Jun 2016
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.
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Specificații

ISBN-13: 9783319302638
ISBN-10: 3319302639
Pagini: 250
Ilustrații: XV, 487 p. 182 illus., 160 illus. in color.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.92 kg
Ediția: 1st ed. 2016
Editura: Springer
Colecția Springer
Seria Studies in Big Data

Locul publicării: Cham, Switzerland

Cuprins



Recenzii

“It can be used as a reference book on big data, to obtain a broad view of the direction and landscape. In addition, it can be used by specialists in specific areas of big data, especially optimization-related areas. In this respect, the preview of chapter titles and brief explanations provided in this review reveal specific areas of interest for the intended specialists. I like this edited volume and recommend it.” (M. M. Tanik, Computing Reviews, January, 2017)

Textul de pe ultima copertă

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.

Caracteristici

Presents recent developments and challenges in big data optimization
Collects various recent algorithms in large-scale optimization all in one book
Presents useful big data optimization applications in a variety of industries, both for academics and practitioners
Include some guideline to use cloud computing and Hadoop in large-scale and big data optimization
Includes supplementary material: sn.pub/extras