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- W2963700504 abstract "In this letter, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm. We explicitly model the update rules of the incremental proximal gradient method and develop a systematic approach to propagate the uncertainty of the solution estimate over iterations. The PIPG algorithm takes the form of Bayesian filtering updates for a state-space model constructed by using the cost function. Our framework makes it possible to utilize well-known exact or approximate Bayesian filters, such as Kalman or extended Kalman filters, to solve large-scale regularized optimization problems." @default.
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- W2963700504 date "2019-08-01" @default.
- W2963700504 modified "2023-10-18" @default.
- W2963700504 title "A Probabilistic Incremental Proximal Gradient Method" @default.
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- W2963700504 doi "https://doi.org/10.1109/lsp.2019.2926926" @default.
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