20 citations · 68 across the 21 of their papers we have counts for
4 papers · 2 filters
Improving the Last-Iterate Guarantees of Anytime Algorithms for Stochastic Monotone Variational Inequalities
Jun-Hyun Kim, Ahmet Alacaoglu
We analyze a stochastic algorithm with Halpern-type anchoring for constrained convex-concave problems and monotone variational inequalities. This single-loop and single-call algori…
How to Make the Gradient Mapping Small for Constrained Stochastic Min-Max Problems and Beyond
Ahmet Alacaoglu
We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone variational inequalities. We focus…
Solving Stochastic Variational Inequalities without the Bounded Variance Assumption
Ahmet Alacaoglu, Jun-Hyun Kim
We analyze algorithms for solving stochastic variational inequalities (VI) without the bounded variance or bounded domain assumptions, where our main focus is min-max optimization…
Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
Kevin Kurian Thomas Vaidyan, Michael P. Friedlander, Ahmet Alacaoglu
We analyze two classical algorithms for solving additively composite convex optimization problems where the objective is the sum of a smooth term and a nonsmooth regularizer: proxi…