Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics

Author

Cevher, Volkan ; Becker, Steffen ; Schmidt, Martin

Author_Institution

Electr. Eng., Swiss Inst. of Technol., Lausanne, Switzerland

Volume

31

Issue

5

fYear

2014

fDate

Sept. 2014

Firstpage

32

Lastpage

43

Abstract

This article reviews recent advances in convex optimization algorithms for big data, which aim to reduce the computational, storage, and communications bottlenecks. We provide an overview of this emerging field, describe contemporary approximation techniques such as first-order methods and randomization for scalability, and survey the important role of parallel and distributed computation. The new big data algorithms are based on surprisingly simple principles and attain staggering accelerations even on classical problems.

Keywords

Big Data; approximation theory; convex programming; data analysis; parallel processing; randomised algorithms; Big Data analytics; communications bottleneck reduction; computational bottleneck reduction; contemporary approximation techniques; convex optimization; distributed computation; first-order methods; parallel algorithm; parallel computation; randomized algorithm; storage bottleneck reduction; Big data; Convex functions; Data processing; Gradient methods; Information analysis; Random processes; Scalability; Signal processing algorithms;

fLanguage

English

Journal_Title

Signal Processing Magazine, IEEE

Publisher

ieee

ISSN

1053-5888

Type

jour

DOI

10.1109/MSP.2014.2329397

Filename

6879615