A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing

Mingyi Hong, Meisam Razaviyayn, Zhi Quan Luo, Jong Shi Pang

Research output: Contribution to journalArticlepeer-review

137 Scopus citations

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.

Original languageEnglish (US)
Article number7366709
Pages (from-to)57-77
Number of pages21
JournalIEEE Signal Processing Magazine
Volume33
Issue number1
DOIs
StatePublished - Jan 2016

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