Ruichuan Huang

Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints

We propose smoothed primal-dual algorithms for solving stochastic nonconvex optimization problems with linear \emph{inequality} constraints. Our algorithms are single-loop and only require a single (or two) samples of stochastic gradients at each iteration. A defining feature of our algorithm is tha…