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.
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
Copyright Date
2016
Language
en
File Format
application/pdf
Recommended Citation
Hong, Mingyi; Razaviyayn, Meisam; and Luo, Zhi-Quan, "A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing" (2016). Industrial and Manufacturing Systems Engineering Publications. 87.
https://lib.dr.iastate.edu/imse_pubs/87
Included in
Databases and Information Systems Commons, Industrial Engineering Commons, Systems Engineering Commons
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.