Overview
- 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
Part of the book series: Studies in Big Data (SBD, volume 18)
Buy print copy
About 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.
Similar content being viewed by others
Keywords
Table of contents (20 chapters)
Reviews
Editors and Affiliations
Bibliographic Information
Book Title: Big Data Optimization: Recent Developments and Challenges
Editors: Ali Emrouznejad
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-30265-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-30263-8Published: 07 June 2016
Softcover ISBN: 978-3-319-80765-2Published: 30 May 2018
eBook ISBN: 978-3-319-30265-2Published: 26 May 2016
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XV, 487
Number of Illustrations: 22 b/w illustrations, 160 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Operations Research/Decision Theory