Time-Varying Convex Optimization: Time-Structured Algorithms and Applications

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....

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Bibliographic Details
Published in:Proceedings of the IEEE Vol. 108; no. 11; pp. 2032 - 2048
Main Authors: Simonetto, Andrea, Dall'Anese, Emiliano, Paternain, Santiago, Leus, Geert, Giannakis, Georgios B.
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
Computational geometry
Convergence of numerical methods
Convex functions
Convexity
Heuristic algorithms
Machine learning
Optimization
optimization methods
Power system dynamics
Prediction algorithms
Robotics
Time-varying systems
ISSN:0018-9219, 1558-2256
Online Access:Get full text
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Summary: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.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2020.3003156