Big Data Optimization: Recent Developments and Challenges
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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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2016, 2016
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| Edition: | 1st ed. 2016 |
| Series: | Studies in Big Data
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| 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 |
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| Physical Description: | XV, 487 p. 182 illus., 160 illus. in color online resource |
| ISBN: | 9783319302652 |