期刊
PROCEEDINGS OF THE IEEE
卷 108, 期 11, 页码 2032-2048出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2020.3003156
关键词
Optimization; Heuristic algorithms; Prediction algorithms; Convex functions; Power system dynamics; Time-varying systems; Approximation algorithms; Convergence of numerical methods; optimization methods
资金
- National Science Foundation (NSF) [1941896]
- NSF [1711471, 1901134]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1941896] Funding Source: National Science Foundation
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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