Shi, Guanya and Lin, Yiheng and Chung, Soon-Jo and Yue, Yisong and Wierman, Adam (2020) Online Optimization with Memory and Competitive Control. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020). Neural Information Processing Foundation , La Jolla, CA, pp. 1-12. ISBN 9781713829546. https://resolver.caltech.edu/CaltechAUTHORS:20221222-183256740
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Abstract
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hitting cost and a switching cost that depends on the previous p decisions. This setting generalizes Smoothed Online Convex Optimization. The proposed approach, Optimistic Regularized Online Balanced Descent, achieves a constant, dimension-free competitive ratio. Further, we show a connection between online optimization with memory and online control with adversarial disturbances. This connection, in turn, leads to a new constant-competitive policy for a rich class of online control problems.
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Additional Information: | This project was supported in part by funding from Raytheon, DARPA PAI, AitF-1637598 and CNS-1518941, with additional support for Guanya Shi provided by the Simoudis Discovery Prize. We see no ethical concerns related to the results in this paper. | ||||||||||||
Group: | GALCIT | ||||||||||||
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Record Number: | CaltechAUTHORS:20221222-183256740 | ||||||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20221222-183256740 | ||||||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||
ID Code: | 118582 | ||||||||||||
Collection: | CaltechAUTHORS | ||||||||||||
Deposited By: | George Porter | ||||||||||||
Deposited On: | 22 Dec 2022 23:54 | ||||||||||||
Last Modified: | 22 Dec 2022 23:54 |
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