Big Data Optimization: Recent Developments and Challenges
Business & Finance, Management & Leadership, Operations Research, Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
| Author: | ISBN: | 9783319302652 | |
| Publisher: | Springer International Publishing | Publication: | May 26, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | |
| ISBN: | 9783319302652 |
| Publisher: | Springer International Publishing |
| Publication: | May 26, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
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.
