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This article presents a powerful algorithmic framework for big dataoptimization, called the Block Successive Upper bound Minimization (BSUM). TheBSUM includes as special cases many well-known methods for analyzing massivedata sets, such as the Block Coordinate Descent (BCD), the Convex-ConcaveProcedure (CCCP), the Block Coordinate Proximal Gradient (BCPG) method, theNonnegative Matrix Factorization (NMF), the Expectation Maximization (EM)method and so on. In this article, various features and properties of the BSUMare discussed from the viewpoint of design flexibility, computationalefficiency, parallel/distributed implementation and the required communicationoverhead. Illustrative examples from networking, signal processing and machinelearning are presented to demonstrate the practical performance of the BSUMframework

A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data