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A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization

Tang, Yujie and Dall’Anese, Emiliano and Bernstein, Andrey and Low, Steven H. (2018) A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization. In: 2018 IEEE Conference on Decision and Control (CDC). IEEE , Piscataway, NJ, pp. 3244-3250. ISBN 978-1-5386-1395-5. http://resolver.caltech.edu/CaltechAUTHORS:20190204-111158128

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Abstract

This paper considers time-varying nonconvex optimization problems, utilized to model optimal operational trajectories of systems governed by possibly nonlinear physical or logical models. Algorithms for tracking a Karush-Kuhn-Tucker point are synthesized, based on a regularized primal-dual gradient method. In particular, the paper proposes a feedback-based primal-dual gradient algorithm, where analytical models for system state or constraints are replaced with actual measurements. When cost and constraint functions are twice continuously differentiable, conditions for the proposed algorithms to have bounded tracking error are derived, and a discussion of their practical implications is provided. Illustrative numerical simulations are presented for an application in power systems.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/CDC.2018.8619225DOIArticle
https://arxiv.org/abs/1812.00613arXivDiscussion Paper
ORCID:
AuthorORCID
Tang, Yujie0000-0002-4921-8372
Low, Steven H.0000-0001-6476-3048
Alternate Title:Running Primal-Dual Gradient Method for Time-Varying Nonconvex Problems
Additional Information:© 2018 IEEE. This work was supported by the Advanced Research Projects Agency-Energy (ARPA-E) under the Network Optimized Distributed Energy Systems (NODES) program.
Funders:
Funding AgencyGrant Number
Advanced Research Projects Agency-Energy (ARPA-E)UNSPECIFIED
Record Number:CaltechAUTHORS:20190204-111158128
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190204-111158128
Official Citation:Y. Tang, E. Dall'Anese, A. Bernstein and S. H. Low, "A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization," 2018 IEEE Conference on Decision and Control (CDC), FL, USA, 2018, pp. 3244-3250. doi: 10.1109/CDC.2018.8619225
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92627
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Feb 2019 20:48
Last Modified:04 Feb 2019 20:48

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