Time-Varying Convex Optimization
Time-Structured Algorithms and Applications
Andrea Simonetto (IBM Research Ireland)
Emiliano Dall'Anese (University of Colorado - Boulder)
Santiago Paternain (University of Pennsylvania)
Geert Leus (TU Delft - Signal Processing Systems)
Georgios B. Giannakis (University of Minnesota)
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Abstract
Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch algorithms for medium-scale problems to challenging dynamic, time-varying, and even huge-size settings. This is driven by technological transformations that converted infrastructural and social platforms into complex and dynamic networked systems with even pervasive sensing and computing capabilities. This article reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to performing both algorithmic development and performance analysis. It offers a comprehensive overview of available tools and methods and unveils open challenges in application domains of broad range of interest. The real-world examples presented include smart power systems, robotics, machine learning, and data analytics, highlighting domain-specific issues and solutions. The ultimate goal is to exemplify wide engineering relevance of analytical tools and pertinent theoretical foundations.