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Springer Big Data Optimization: Recent Developments and Challenges

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Description

Big Data Optimization: Recent Developments and Challenges provides a comprehensive foundation for anyone working within the big data landscape. This volume introduces novel optimization algorithms and codes specifically designed to function effectively in big data settings. It serves as a practical resource for both academics and practitioners looking to apply these methods to real-world scenarios. By presenting applications across a wide variety of industries, this book helps researchers analyze large-scale data more effectively. The content covers several essential optimization algorithms, including convergent parallel algorithms and limited methods, to help users navigate complex data environments. Whether you are working in industry, academia, or government, this book offers the technical background needed to benefit from large-scale data analysis and optimization.

Key Features

Introduces novel optimization algorithms and codes designed specifically for big data environments.

Provides technical background necessary for researchers aiming to analyze large-scale data sets.

Covers various optimization algorithms including convergent parallel algorithms and limited methods.

Offers practical applications across a wide variety of industries for real-world use.

Supports professionals and academics in society, industry, academia, and government sectors.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
18 May 2016
Listed Since
21 January 2016

Barcode

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