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 | ||||||
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Additional Information: | © 2019 IEEE. | ||||||
DOI: | 10.1109/cdc40024.2019.9028955 | ||||||
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: | 16 Nov 2021 18:42 |
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