8 citations · 13 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Charging station optimization for balanced electric car sharing
Antoine Deza, Kai Huang, Michael R. Metel
This work focuses on finding optimal locations for charging stations for one-way electric car sharing programs. The relocation of vehicles by a service staff is generally required…
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…