Time-Varying Convex Optimization

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

Simonetto, Andrea (IBM Research Ireland)
Dall'Anese, Emiliano (University of Colorado)
Paternain, Santiago (University of Pennsylvania)
Leus, G.J.T. (TU Delft Signal Processing Systems)
Giannakis, Georgios B. (University of Minnesota)

2020

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.

Convergence of numerical methods
optimization methods

http://resolver.tudelft.nl/uuid:a7ea98c0-1e3b-4b4c-b82c-b9462a3b55f1

https://doi.org/10.1109/JPROC.2020.3003156

2021-01-03

0018-9219

Institute of Electrical and Electronics Engineers. Proceedings, 108 (11), 2032-2048

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2020 Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain, G.J.T. Leus, Georgios B. Giannakis