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

Andrea Simonetto; Emiliano Dall'Anese; Santiago Paternain; Geert Leus; Georgios B. Giannakis

doi:10.1109/JPROC.2020.3003156

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

Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain, Geert Leus, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Contribution to journal › Review article › peer-review

37 Scopus citations

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.

Original language	English (US)
Article number	9133310
Pages (from-to)	2032-2048
Number of pages	17
Journal	Proceedings of the IEEE
Volume	108
Issue number	11
DOIs	https://doi.org/10.1109/JPROC.2020.3003156
State	Published - Nov 2020

Bibliographical note

Funding Information:
Manuscript received December 14, 2019; revised March 31, 2020; accepted June 9, 2020. Date of publication July 3, 2020; date of current version October 27, 2020. The work of Emiliano Dall’Anese was supported in part by the National Science Foundation (NSF) under Grant 1941896. The work of Georgios B. Giannakis was supported in part by NSF under Grant 1711471 and Grant 1901134. (Corresponding author: Andrea Simonetto.) Andrea Simonetto is with the Optimization and Control Group, IBM Research Ireland, Dublin 15, Ireland (e-mail: andrea.simonetto@ibm.com). Emiliano Dall’Anese is with the College of Engineering and Applied Science, University of Colorado Boulder, Boulder, CO 80309 USA (e-mail: emiliano.dallanese@colorado.edu). Santiago Paternain is with the Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: spater@seas.upenn.edu). Geert Leus is with the Faculty of Electrical, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands (e-mail: g.j.t.leus@tudelft.nl). Georgios B. Giannakis is with the Digital Technology Center, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: georgios@umn.edu).

Publisher Copyright:
© 2020 IEEE.

Keywords

Convergence of numerical methods
optimization methods

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10.1109/JPROC.2020.3003156

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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.",

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

Abstract

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