37 citations · 38 across the 2 of their papers we have counts for
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math.OC2026
Achieving Directional-Stationarity from a Single Random Direction Step
Dan Greenstein, Nadav Hallak
This paper addresses the challenge of obtaining strong optimality guarantees in constrained nonsmooth nonconvex optimization under mild regularity conditions, namely local Lipschit…
math.OC2023
An Augmented Lagrangian Approach to Composite Problems with a Random Linear Operator
Dan Greenstein, Nadav Hallak
We consider the minimization of a sum of a smooth function with a nonsmooth composite function, where the composition is applied on a random linear mapping. This random composite m…
math.OC2020★ 37 cited
On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems
Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis +1
This paper analyzes the trajectories of stochastic gradient descent (SGD) to help understand the algorithm's convergence properties in non-convex problems. We first show that the s…