Stochastic first-order methods with multi-extrapolated momentum for highly smooth unconstrained optimization

In this paper we consider an unconstrained stochastic optimization problem where the objective function exhibits a high order of smoothness. In particular, we propose a stochastic first-order method (SFOM) with multi-extrapolated momentum, in which multiple extrapolations are performed in each iteration, followed by a momentum step based on these extrapolations. We show that our proposed … Read more

A momentum-based linearized augmented Lagrangian method for nonconvex constrained stochastic optimization

Nonconvex constrained stochastic optimization has emerged in many important application areas. Subject to general functional constraints it minimizes the sum of an expectation function and a nonsmooth regularizer. Main challenges arise due to the stochasticity in the random integrand and the possibly nonconvex functional constraints. To address these issues we propose a momentum-based linearized augmented … Read more