We study structured convex optimization problems, with additive objective $ r:= p+ q $,
where $ r $ is ($\mu $-strongly) convex, $ q $ is $ L_q $-smooth and convex, and $ p $ is …

We study finite-sum distributed optimization problems involving a master node and $ n-1$
local nodes under the popular $\delta $-similarity and $\mu $-strong convexity conditions …

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 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 …

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

Inspired by the recent work FedNL (Safaryan et al, FedNL: Making Newton-Type Methods
Applicable to Federated Learning), we propose a new communication efficient second-order …

We present a new accelerated stochastic second-order method that is robust to both
gradient and Hessian inexactness, which occurs typically in machine learning. We establish …

In this paper, we propose Cubic Regularized Quasi-Newton Methods for (strongly)
starconvex and Accelerated Cubic Regularized Quasi-Newton for convex optimization. The …

It is well known since the works of Newton [64] and Kantorovich [45] that the second-order
derivative of the objective function can be used in numerical algorithms for solving …

Variational inequalities are an important tool, which includes minimization, saddles, games,
fixed-point problems. Modern large-scale and computationally expensive practical …