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

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

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)
33 Downloads (Pure)


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 languageEnglish
Article number9133310
Pages (from-to)2032-2048
Number of pages17
JournalProceedings of the IEEE
Issue number11
Publication statusPublished - 2020

Bibliographical note

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.


  • Convergence of numerical methods
  • optimization methods


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