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A Second-Order Saddle Point Method for Time-Varying Optimization

Tang, Yujie and Low, Steven (2019) A Second-Order Saddle Point Method for Time-Varying Optimization. In: 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 3928-3935. ISBN 9781728113982. https://resolver.caltech.edu/CaltechAUTHORS:20200911-133139018

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Abstract

Time-varying optimization studies algorithms that can track solutions of optimization problems that evolve with time. A typical time-varying optimization algorithm is implemented in a running fashion in the sense that the underlying optimization problem is updated during the iterations of the algorithm, and is especially suitable for optimizing large-scale fast varying systems. In this paper, we propose and analyze a second-order method for time-varying optimization. Each iteration of the proposed method can be formulated as solving a quadratic-like saddle point problem that incorporates curvature information. Theoretical results on the tracking performance of the proposed method are presented, and discussions on their implications and comparison with existing second-order and first-order methods are also provided.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/cdc40024.2019.9028955DOIArticle
ORCID:
AuthorORCID
Tang, Yujie0000-0002-4921-8372
Low, Steven0000-0001-6476-3048
Additional Information:© 2019 IEEE.
Record Number:CaltechAUTHORS:20200911-133139018
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200911-133139018
Official Citation:Y. Tang and S. Low, "A Second-Order Saddle Point Method for Time-Varying Optimization," 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, 2019, pp. 3928-3935, doi: 10.1109/CDC40024.2019.9028955
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105356
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:11 Sep 2020 22:26
Last Modified:11 Sep 2020 22:26

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