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arXiv · 2003.08902

Fast proximal algorithms for nonsmooth convex optimization

Adam Ouorou
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Abstract

In the lines of our approach in \cite{Ouorou2019}, where we exploit Nesterov fast gradient concept \cite{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 \cite{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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Adam Ouorou. 2020-03-19. Fast proximal algorithms for nonsmooth convex optimization. https://doi.org/10.1016/s0167-6377(20)30146-2

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