We propose a new homotopy-based conditional gradient method for solving
...
We consider decentralized stochastic variational inequalities where the
...
Thanks to their practical efficiency and random nature of the data,
stoc...
We consider distributed convex-concave saddle point problems over arbitr...
We propose a distributed cubic regularization of the Newton method for
s...
First-order methods for solving convex optimization problems have been a...
Motivated by recent increased interest in optimization algorithms for
no...
In recent years, the importance of saddle-point problems in machine lear...
We study the computation of non-regularized Wasserstein barycenters of
p...
Projection-free optimization via different variants of the Frank-Wolfe (...
Many problems in statistical learning, imaging, and computer vision invo...
Alternating minimization (AM) optimization algorithms have been known fo...
We study the complexity of approximating Wassertein barycenter of m
disc...
We study the problem of decentralized distributed computation of a discr...
We consider smooth stochastic convex optimization problems in the contex...
We propose a new class-optimal algorithm for the distributed computation...
We consider an unconstrained problem of minimization of a smooth convex
...
We analyze two algorithms for approximating the general optimal transpor...
In this paper, we consider smooth convex optimization problems with simp...