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Online Optimization with Memory and Competitive Control

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.

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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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription ItemDiscussion Paper
Shi, Guanya0000-0002-9075-3705
Lin, Yiheng0000-0001-6524-2877
Chung, Soon-Jo0000-0002-6657-3907
Yue, Yisong0000-0001-9127-1989
Wierman, Adam0000-0002-5923-0199
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.
Funding AgencyGrant Number
Raytheon CompanyUNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Simoudis Discovery PrizeUNSPECIFIED
Record Number:CaltechAUTHORS:20221222-183256740
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118582
Deposited By: George Porter
Deposited On:22 Dec 2022 23:54
Last Modified:22 Dec 2022 23:54

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