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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…
math.OC2025
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…