5 papers
Towards Weaker Variance Assumptions for Stochastic Optimization
Ahmet Alacaoglu, Yura Malitsky, Stephen J. Wright
We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed…
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…