A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processingIEEE Signal Processing Magazine
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AbstractThis 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.
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Citation InformationMingyi 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/