In this paper, we propose a new accelerated stochastic first-order method called clipped-
SSTM for smooth convex stochastic optimization with heavy-tailed distributed noise in …

Motivated by recent increased interest in optimization algorithms for non-convex
optimization in application to training deep neural networks and other optimization problems …

Gradient-free/zeroth-order methods for black-box convex optimization have been
extensively studied in the last decade with the main focus on oracle calls complexity. In this …

We consider stochastic convex optimization problems with affine constraints and develop
several methods using either primal or dual approach to solve it. In the primal case, we use …

We consider distributed stochastic variational inequalities (VIs) on unbounded domains with
the problem data that is heterogeneous (non-IID) and distributed across many devices. We …

We introduce primal and dual stochastic gradient oracle methods for decentralized convex
optimization problems. Both for primal and dual oracles, the proposed methods are optimal …

In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …

Consider a convex optimization problem min x∈ Q⊆ Rd f (x)(1) with convex feasible set Q
and convex objective f possessing the zeroth-order (gradient/derivativefree) oracle [83]. The …

In this paper, we study the standard formulation of an optimization problem when the
computation of gradient is not available. Such a problem can be classified as a “black box” …

In this paper we consider the unconstrained minimization problem of a smooth function in
R^n in a setting where only function evaluations are possible. We design a novel …