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• W3201501162 abstract "We study the convergence of the Proximal-Gradient algorithm for convex composite problems when both the gradient and the proximal mapping are computed approximately. This scenario occurs when the gradient is computationally expensive and the proximal operator is not available in closed form and may be computed only up to a certain fixed precision. We establish tight deterministic bounds and propose new probabilistic upper bounds on the suboptimality of the function values along the iterations under some statistical assumptions on the perturbed iterates. We use the Proximal-Gradient algorithm to solve randomly generated LASSO problems while varying the fixed-point machine representation and the proximal computation precision." @default.
• W3201501162 created "2021-09-27" @default.
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• W3201501162 date "2021-09-01" @default.
• W3201501162 modified "2023-09-24" @default.
• W3201501162 title "Approximate Proximal-Gradient Methods" @default.
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• W3201501162 doi "https://doi.org/10.1109/sspd51364.2021.9541509" @default.
• W3201501162 hasPublicationYear "2021" @default.
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