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Article
A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing
IEEE Signal Processing Magazine
  • Mingyi Hong, Iowa State University
  • Meisam Razaviyayn, University of Minnesota - Twin Cities
  • Zhi-Quan Luo, University of Minnesota - Twin Cities
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
1-1-2016
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
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Copyright Owner
IEEE
Language
en
File Format
application/pdf
Citation Information
Mingyi Hong, Meisam Razaviyayn and Zhi-Quan Luo. "A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing" IEEE Signal Processing Magazine Vol. 33 Iss. 1 (2016) p. 57 - 77
Available at: http://works.bepress.com/mingyi_hong/20/