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- W3159351664 abstract "We study iterative regularization for linear models, when the bias is convex but not necessarily strongly convex. We characterize the stability properties of a primal-dual gradient based approach, analyzing its convergence in the presence of worst case deterministic noise. As a main example, we specialize and illustrate the results for the problem of robust sparse recovery. Key to our analysis is a combination of ideas from regularization theory and optimization in the presence of errors. Theoretical results are complemented by experiments showing that state-of-the-art performances can be achieved with considerable computational speed-ups." @default.
- W3159351664 created "2021-05-10" @default.
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- W3159351664 date "2021-04-01" @default.
- W3159351664 modified "2023-09-25" @default.
- W3159351664 title "Iterative regularization for convex regularizers" @default.
- W3159351664 hasPublicationYear "2021" @default.
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