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• W2945487152 abstract "We aim to solve a structured convex optimization problem, where a nonsmooth function is composed with a linear operator. When opting for full splitting schemes, usually, primal-dual type methods are employed as they are effective and also well studied. However, under the additional assumption of Lipschitz continuity of the nonsmooth function which is composed with the linear operator we can derive novel algorithms through regularization via the Moreau envelope. Furthermore, we tackle large scale problems by means of stochastic oracle calls, very similar to stochastic gradient techniques. Applications to total variational denoising and deblurring are provided." @default.
• W2945487152 created "2019-05-29" @default.
• W2945487152 creator A5011677215 @default.
• W2945487152 creator A5075440320 @default.
• W2945487152 date "2019-05-16" @default.
• W2945487152 modified "2023-10-17" @default.
• W2945487152 title "Variable smoothing for convex optimization problems using stochastic gradients." @default.
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• W2945487152 hasPublicationYear "2019" @default.
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