Stochastic gradient methods (SGMs) have been widely used for solving stochastic optimization problems. A majority of existing works assume no constraints or easy-to-project constraints. In this paper, we consider convex problems with expectation For these problems, it is often extremely expensive to perform projection onto the feasible set. Several SGMs in literature can be applied solve expect...
