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Online Optimization with Feedback Delay and Nonlinear Switching Cost

Pan, Weici and Shi, Guanya and Lin, Yiheng and Wierman, Adam (2022) Online Optimization with Feedback Delay and Nonlinear Switching Cost. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6 (1). Art. No. 17. ISSN 2476-1249. doi:10.1145/3508037. https://resolver.caltech.edu/CaltechAUTHORS:20220302-699021182

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Abstract

We study a variant of online optimization in which the learner receives k-round delayed feedback about hitting cost and there is a multi-step nonlinear switching cost, i.e., costs depend on multiple previous actions in a nonlinear manner. Our main result shows that a novel Iterative Regularized Online Balanced Descent (iROBD) algorithm has a constant, dimension-free competitive ratio that is O(L^(2k)), where L is the Lipschitz constant of the switching cost. Additionally, we provide lower bounds that illustrate the Lipschitz condition is required and the dependencies on k and L are tight. Finally, via reductions, we show that this setting is closely related to online control problems with delay, nonlinear dynamics, and adversarial disturbances, where iROBD directly offers constant-competitive online policies.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3508037DOIArticle
https://arxiv.org/abs/2111.00095arXivDiscussion Paper
https://resolver.caltech.edu/CaltechAUTHORS:20220304-172341428Related ItemConference Paper
ORCID:
AuthorORCID
Shi, Guanya0000-0002-9075-3705
Lin, Yiheng0000-0001-6524-2877
Wierman, Adam0000-0002-5923-0199
Additional Information:© 2022 Owner/Author. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Subject Keywords:online learning; online optimization; online control
Issue or Number:1
Classification Code:Computing methodologies → Online learning settings
DOI:10.1145/3508037
Record Number:CaltechAUTHORS:20220302-699021182
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220302-699021182
Official Citation:Weici Pan, Guanya Shi, Yiheng Lin, and Adam Wierman. 2022. Online Optimization with Feedback Delay and Nonlinear Switching Cost. Proc. ACM Meas. Anal. Comput. Syst. 6, 1, Article 17 (March 2022), 34 pages. DOI: https://doi.org/10.1145/3508037
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113677
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:02 Mar 2022 10:38
Last Modified:23 Dec 2022 18:34

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