Campus Units

Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

1-2016

Journal or Book Title

IEEE Signal Processing Magazine

Volume

33

Issue

1

First Page

57

Last Page

77

Research Focus Area(s)

Information Engineering

DOI

10.1109/MSP.2015.2481563

Abstract

This article presents a powerful algorithmic framework for big data optimization, called the block successive upper-bound minimization (BSUM). The BSUM includes as special cases many well-known methods for analyzing massive data sets, such as the block coordinate descent (BCD) method, the convex-concave procedure (CCCP) method, the block coordinate proximal gradient (BCPG) method, the nonnegative matrix factorization (NMF) method, the expectation maximization (EM) method, etc. In this article, various features and properties of the BSUM are discussed from the viewpoint of design flexibility, computational efficiency, parallel/distributed implementation, and the required communication overhead. Illustrative examples from networking, signal processing, and machine learning are presented to demonstrate the practical performance of the BSUM framework.

Comments

This is a manuscript of an article from IEEE Signal Processing Magazine 33 (2016): 57, doi: 10.1109/MSP.2015.2481563. Posted with permission.

Rights

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

Share

COinS