63 citations · 323 across the 46 of their papers we have counts for
9 papers · 1 filter
Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal-dual tensor methods
Pavel Dvurechensky, Petr Ostroukhov, Alexander Gasnikov +2
Motivated, in particular, by the entropy-regularized optimal transport problem, we consider convex optimization problems with linear equality constraints, where the dual objective…
Generalized Self-concordant Hessian-barrier algorithms
Pavel Dvurechensky, Mathias Staudigl, César A. Uribe
Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible s…
Non-Bayesian Social Learning with Gaussian Uncertain Models
James Z. Hare, Cesar Uribe, Lance Kaplan +1
Non-Bayesian social learning theory provides a framework for distributed inference of a group of agents interacting over a social network by sequentially communicating and updating…
Non-Bayesian Social Learning with Uncertain Models
James Z. Hare, Cesar A. Uribe, Lance Kaplan +1
Non-Bayesian social learning theory provides a framework that models distributed inference for a group of agents interacting over a social network. In this framework, each agent it…
Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs
César A. Uribe, James Z. Hare, Lance Kaplan +1
We study the problem of non-Bayesian social learning with uncertain models, in which a network of agents seek to cooperatively identify the state of the world based on a sequence o…
Resilient Distributed Optimization Algorithms for Resource Allocation
Cesar A. Uribe, Hoi-To Wai, Mahnoosh Alizadeh
Distributed algorithms provide flexibility over centralized algorithms for resource allocation problems, e.g., cyber-physical systems. However, the distributed nature of these algo…
Gradient Methods for Problems with Inexact Model of the Objective
Fedor Stonyakin, Darina Dvinskikh, Pavel Dvurechensky +8
We consider optimization methods for convex minimization problems under inexact information on the objective function. We introduce inexact model of the objective, which as a parti…
On Primal-Dual Approach for Distributed Stochastic Convex Optimization over Networks
Darina Dvinskikh, Eduard Gorbunov, Alexander Gasnikov +2
We introduce a primal-dual stochastic gradient oracle method for distributed convex optimization problems over networks. We show that the proposed method is optimal in terms of com…
On the Complexity of Approximating Wasserstein Barycenter
Alexey Kroshnin, Darina Dvinskikh, Pavel Dvurechensky +3
We study the complexity of approximating Wassertein barycenter of discrete measures, or histograms of size by contrasting two alternative approaches, both using entropic re…