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Adaptive Composite Online Optimization: Predictions in Static and Dynamic Environments
- Publication Year :
- 2023
-
Abstract
- In the past few years, online convex optimization (OCO) has received notable attention in the control literature thanks to its flexible real-time nature and powerful performance guarantees. In this article, we propose new step-size rules and OCO algorithms that simultaneously exploit gradient predictions, function predictions and dynamics, features particularly pertinent to control applications. The proposed algorithms enjoy static and dynamic regret bounds in terms of the dynamics of the reference action sequence, gradient prediction error, and function prediction error, which are generalizations of known regularity measures from the literature. We present results for both convex and strongly convex costs. We validate the performance of the proposed algorithms in a trajectory tracking case study, as well as portfolio optimization using real-world datasets.<br />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.<br />Team Peyman Mohajerin Esfahani<br />Team Tamas Keviczky
Details
- Database :
- OAIster
- Notes :
- Zattoni Scroccaro, P. (author), Sharifi K., Arman (author), Mohajerin Esfahani, P. (author)
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1390839777
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1109.TAC.2023.3237486