A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data
arXiv:1511.02746
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), the Convex-Concave Procedure (CCCP), the Block Coordinate Proximal Gradient (BCPG) method, the Nonnegative Matrix Factorization (NMF), the Expectation Maximization (EM) method and so on. 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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- On the Pervasiveness of Difference-Convexity in Optimization and Statistics
- Gradient-Free Multi-Agent Nonconvex Nonsmooth Optimization
- Convergence Analysis and Design of Multi-block ADMM via Switched Control Theory