A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization
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.
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.Attached Files
Submitted - 1812.00613.pdf
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Additional details
- Alternative title
- Running Primal-Dual Gradient Method for Time-Varying Nonconvex Problems
- Eprint ID
- 92627
- DOI
- 10.1109/CDC.2018.8619225
- Resolver ID
- CaltechAUTHORS:20190204-111158128
- Advanced Research Projects Agency-Energy (ARPA-E)
- Created
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2019-02-04Created from EPrint's datestamp field
- Updated
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2023-03-16Created from EPrint's last_modified field