Published December 2020
| public
Book Section - Chapter
Online Optimization with Memory and Competitive Control
Chicago
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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.
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.Additional details
- Eprint ID
- 118582
- Resolver ID
- CaltechAUTHORS:20221222-183256740
- Raytheon Company
- Defense Advanced Research Projects Agency (DARPA)
- NSF
- CCF-1637598
- NSF
- CNS-1518941
- Simoudis Discovery Prize
- Created
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2022-12-22Created from EPrint's datestamp field
- Updated
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2022-12-22Created from EPrint's last_modified field
- Caltech groups
- GALCIT