8 citations · 17 across the 18 of their papers we have counts for
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math.OC2019
Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization
Michael R. Metel, Akiko Takeda
This paper focuses on stochastic proximal gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer and convex constraints. To the…
math.OC2019★ 8 cited
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization
Michael R. Metel, Akiko Takeda
Our work focuses on stochastic gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer. Research on this class of problem is quit…