20 citations · 68 across the 21 of their papers we have counts for
23 papers
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…
Decentralized Optimization with Topology-Independent Communication
Ying Lin, Yao Kuang, Ahmet Alacaoglu +1
Distributed optimization requires nodes to coordinate, yet full synchronization scales poorly. When nodes collaborate through pairwise regularizers, standard methods demand…
Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints
Ruichuan Huang, Jiawei Zhang, Ahmet Alacaoglu
We propose smoothed primal-dual algorithms for solving stochastic and smooth nonconvex optimization problems with linear inequality constraints. Our algorithms are single-loop and…