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 …

This work introduces DADAO: the first decentralized, accelerated, asynchronous, primal, first-
order algorithm to minimize a sum of $ L $-smooth and $\mu $-strongly convex functions …

We consider distributed convex-concave saddle point problems over arbitrary connected
undirected networks and propose a decentralized distributed algorithm for their solution. The …

We propose ADOM–an accelerated method for smooth and strongly convex decentralized
optimization over time-varying networks. ADOM uses a dual oracle, ie, we assume access to …

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 study distributed (strongly convex) optimization problems over a network of agents, with
no centralized nodes. The loss functions of the agents are assumed to be similar, due to …

We consider decentralized optimization problems where one aims to minimize a sum of
convex smooth objective functions distributed between nodes in the network. The links in the …

Large-scale saddle-point problems arise in such machine learning tasks as GANs and linear
models with affine constraints. In this paper, we study distributed saddle-point problems with …

First-order methods for solving convex optimization problems have been at the forefront of
mathematical optimization in the last 20 years. The rapid development of this important class …

We propose a distributed cubic regularization of the Newton method for solving
(constrained) empirical risk minimization problems over a network of agents, modeled as …