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• W3137992723 abstract "This article considers a time-varying optimization problem associated with a network of systems, with each of the systems shared by (and affecting) a number of individuals. The objective is to minimize cost functions associated with the individuals' preferences, which are unknown, subject to time-varying constraints that capture physical or operational limits of the network. To this end, this article develops a distributed online optimization algorithm with concurrent learning of the cost functions. The cost functions are learned on-the-fly based on the users' feedback (provided at irregular intervals) by leveraging tools from the shape-constrained Gaussian process. The online algorithm is based on a primal–dual method and acts effectively in a closed-loop fashion where: First, users' feedback is utilized to estimate the cost, and second, measurements from the network are utilized in the algorithmic steps to bypass the need for sensing of (unknown) exogenous inputs of the network. The performance of the algorithm is analyzed in terms of dynamic network regret and constraint violation. Numerical examples are presented in the context of real-time optimization of distributed energy resources." @default.
• W3137992723 created "2021-03-29" @default.
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• W3137992723 date "2023-03-01" @default.
• W3137992723 modified "2023-09-24" @default.
• W3137992723 title "Time-Varying Optimization of Networked Systems With Human Preferences" @default.
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• W3137992723 doi "https://doi.org/10.1109/tcns.2022.3203467" @default.
• W3137992723 hasPublicationYear "2023" @default.
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