62 citations · 346 across the 51 of their papers we have counts for
24 papers · 1 filter
STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization
Kfir Y. Levy, Ali Kavis, Volkan Cevher
In this work we investigate stochastic non-convex optimization problems where the objective is an expectation over smooth loss functions, and the goal is to find an approximate sta…
A first-order primal-dual method with adaptivity to local smoothness
Maria-Luiza Vladarean, Yura Malitsky, Volkan Cevher
We consider the problem of finding a saddle point for the convex-concave objective , where is a convex function with locally…
Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher
We introduce a randomly extrapolated primal-dual coordinate descent method that adapts to sparsity of the data matrix and the favorable structures of the objective function. Our me…
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…
The limits of min-max optimization algorithms: convergence to spurious non-critical sets
Ya-Ping Hsieh, Panayotis Mertikopoulos, Volkan Cevher
Compared to ordinary function minimization problems, min-max optimization algorithms encounter far greater challenges because of the existence of periodic cycles and similar phenom…
A Newton Frank-Wolfe Method for Constrained Self-Concordant Minimization
Deyi Liu, Volkan Cevher, Quoc Tran-Dinh
We demonstrate how to scalably solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO) over the constraint set. We prove that the…