Proximal Gradient Methods with Adaptive Subspace Samplings

Abstract

Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions. In this paper, we propose a randomized proximal gradient method harnessing this underlying structure. We introduce two key components$:$ i) a random subspace proximal gradient algorithm; ii) an identification-based sampling of the subspaces. Their interplay brings a significant performance improvement on typical learning problems in terms of dimensions explored.

Publication
to appear in MOOR
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Dmitry Grishchenko
PhD student in Applied Mathematics