Running Primal-Dual Gradient Method for Time-Varying Nonconvex Problems
Journal Article
·
· SIAM Journal on Control and Optimization
This paper focuses on a time-varying constrained nonconvex optimization problem, and considers the synthesis and analysis of online regularized primal-dual gradient methods to track a Karush-Kuhn-Tucker (KKT) trajectory. The proposed regularized primal-dual gradient method is implemented in a running fashion, in the sense that the underlying optimization problem changes during the execution of the algorithms. In order to study its performance, we first derive its continuous-time limit as a system of differential inclusions. We then study sufficient conditions for tracking a KKT trajectory, and also derive asymptotic bounds for the tracking error (as a function of the time-variability of a KKT trajectory). Further, we provide a set of sufficient conditions for the KKT trajectories not to bifurcate or merge, and also investigate the optimal choice of the parameters of the algorithm. Illustrative numerical results for a time-varying nonconvex problem are provided.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Advanced Research Projects Agency - Energy (ARPA-E)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1884801
- Report Number(s):
- NREL/JA-5D00-83850; MainId:84623; UUID:68a1f3ac-3092-40fe-a48c-3b2beb663092; MainAdminID:65237
- Journal Information:
- SIAM Journal on Control and Optimization, Journal Name: SIAM Journal on Control and Optimization Journal Issue: 4 Vol. 60
- Country of Publication:
- United States
- Language:
- English
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