Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/JPROC.2020.3003156 | Proceedings of the IEEE |
Keywords | DocType | Volume |
Convergence of numerical methods,optimization methods | Journal | 108 |
Issue | ISSN | Citations |
11 | 0018-9219 | 2 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Simonetto | 1 | 14 | 4.35 |
| Emiliano Dall'Anese | 2 | 360 | 38.11 |
| Paternain, S. | 3 | 49 | 10.88 |
| G. Leus | 4 | 4344 | 307.24 |
| Giannakis Georgios B. | 5 | 2 | 0.37 |