Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published December 2018 | Submitted
Book Section - Chapter Open

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


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


Files (2.7 MB)
Name Size Download all
2.7 MB Preview Download

Additional details

August 19, 2023
October 20, 2023