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A Probabilistic Incremental Proximal Gradient Method
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DOI：10.1109/LSP.2019.2926926.png)
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Abstract
En 中文
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.
Keywords:
Probabilistic optimization
stochastic gradient
proximal algorithms
extended Kalman filtering
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9.6
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1.1W
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1.7W
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