Fast proximal algorithms for nonsmooth convex optimization
Abstract
In the lines of our approach in Ouorou2019, where we exploit Nesterov fast gradient concept Nesterov1983 to the Moreau-Yosida regularization of a convex function, we devise new proximal algorithms for nonsmooth convex optimization. These algorithms need no bundling mechanism to update the stability center while preserving the complexity estimates established in Ouorou2019. We report some preliminary computational results on some academic test problem to give a first estimate of their performance in relation with the classical proximal bundle algorithm.
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