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- W3048663310 abstract "We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a specific user. The local objective functions are assumed to have a composite structure and to consist of a known time-varying (engineering) part and an unknown (user-specific) part. Regarding the unknown part, it is assumed to have a known parametric (e.g., quadratic) structure <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>a priori</i> , whose parameters are to be learned <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>along with</i> the evolution of the algorithm. The algorithm is composed of two intertwined components: 1) a dynamic gradient tracking scheme for finding local solution estimates and 2) a recursive least squares scheme for estimating the unknown parameters via user’s noisy feedback on the local solution estimates. The algorithm is shown to exhibit a bounded regret under suitable assumptions. Finally, a numerical example corroborates the theoretical analysis." @default.
- W3048663310 created "2020-08-18" @default.
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- W3048663310 date "2023-01-01" @default.
- W3048663310 modified "2023-10-02" @default.
- W3048663310 title "Distributed Personalized Gradient Tracking With Convex Parametric Models" @default.
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- W3048663310 doi "https://doi.org/10.1109/tac.2022.3147007" @default.
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