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20142026
most citedSimple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization

8 citations · 17 across the 18 of their papers we have counts for

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Showing 2020Show all

2 papers · 1 filter

math.OC2020★ 1 cited

Primal-dual subgradient method for constrained convex optimization problems

Michael R. Metel, Akiko Takeda

This paper considers a general convex constrained problem setting where functions are not assumed to be differentiable nor Lipschitz continuous. Our motivation is in finding a simp…

math.OC2020

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions

Michael R. Metel, Akiko Takeda

This paper presents an extension of stochastic gradient descent for the minimization of Lipschitz continuous loss functions. Our motivation is for use in non-smooth non-convex stoc…