In this paper, we analyze several methods for approximating gradients of noisy functions
using only function values. These methods include finite differences, linear interpolation …

Using backpropagation to compute gradients of objective functions for optimization has
remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation …

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

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 …

We consider optimization methods for convex minimization problems under inexact
information on the objective function. We introduce inexact model of the objective, which as …

We consider a distributed stochastic optimization problem that is solved by a decentralized
network of agents with only local communication between neighboring agents. The goal of …

In the paper, we generalize the approach Gasnikov et al. 2017, which allows to solve
(stochastic) convex optimization problems with an inexact gradient-free oracle, to the convex …

In this paper, we consider two types of problems that have some similarity in their structure,
namely, min-min problems and min-max saddle-point problems. Our approach is based on …