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• W2912446866 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." @default.
• W2912446866 created "2019-02-21" @default.
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• W2912446866 date "2018-12-01" @default.
• W2912446866 modified "2023-09-27" @default.
• W2912446866 title "A Feedback-Based Regularized Primal-Dual Gradient Method for Time-Varying Nonconvex Optimization" @default.
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• W2912446866 doi "https://doi.org/10.1109/cdc.2018.8619225" @default.
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