most citedMini-batch stochastic gradient descent with dynamic sample sizes

5 citations · 5 across the 2 of their papers we have counts for

collaborators

2 papers

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.OC2017★ 5 cited

Mini-batch stochastic gradient descent with dynamic sample sizes

Michael R. Metel

We focus on solving constrained convex optimization problems using mini-batch stochastic gradient descent. Dynamic sample size rules are presented which ensure a descent direction…