Distributed Personalized Gradient Tracking With Convex Parametric Models
نویسندگان
چکیده
We present a distributed optimization algorithm for solving online personalized problems over network of computing and communicating nodes, each which linked to specific user. The local objective functions are assumed have composite structure consist known time-varying (engineering) part an unknown (user-specific) part. Regarding the part, it is parametric (e.g., quadratic) a priori , whose parameters be learned xmlns:xlink="http://www.w3.org/1999/xlink">along with evolution algorithm. composed two intertwined components: 1) dynamic gradient tracking scheme finding solution estimates 2) recursive least squares estimating via user’s noisy feedback on estimates. shown exhibit bounded regret under suitable assumptions. Finally, numerical example corroborates theoretical analysis.
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ژورنال
عنوان ژورنال: IEEE Transactions on Automatic Control
سال: 2023
ISSN: ['0018-9286', '1558-2523', '2334-3303']
DOI: https://doi.org/10.1109/tac.2022.3147007