71 citations · 77 across the 6 of their papers we have counts for
7 papers
An Accelerated Second-Order Method for Distributed Stochastic Optimization
Artem Agafonov, Pavel Dvurechensky, Gesualdo Scutari +4
We consider distributed stochastic optimization problems that are solved with master/workers computation architecture. Statistical arguments allow to exploit statistical similarity…
Newton Method over Networks is Fast up to the Statistical Precision
Amir Daneshmand, Gesualdo Scutari, Pavel Dvurechensky +1
We propose a distributed cubic regularization of the Newton method for solving (constrained) empirical risk minimization problems over a network of agents, modeled as undirected gr…
Distributed Optimization Based on Gradient-tracking Revisited: Enhancing Convergence Rate via Surrogation
Ying Sun, Amir Daneshmand, Gesualdo Scutari
We study distributed multiagent optimization over (directed, time-varying) graphs. We consider the minimization of subject to convex constraints, where is the smooth stro…
Second-order Guarantees of Distributed Gradient Algorithms
Amir Daneshmand, Gesualdo Scutari, Vyacheslav Kungurtsev
We consider distributed smooth nonconvex unconstrained optimization over networks, modeled as a connected graph. We examine the behavior of distributed gradient-based algorithms ne…
Decentralized Dictionary Learning Over Time-Varying Digraphs
Amir Daneshmand, Ying Sun, Gesualdo Scutari +2
This paper studies Dictionary Learning problems wherein the learning task is distributed over a multi-agent network, modeled as a time-varying directed graph. This formulation is r…
Distributed Dictionary Learning
Amir Daneshmand, Gesualdo Scutari, Francisco Facchinei
The paper studies distributed Dictionary Learning (DL) problems where the learning task is distributed over a multi-agent network with time-varying (nonsymmetric) connectivity. Thi…
Hybrid Random/Deterministic Parallel Algorithms for Nonconvex Big Data Optimization
Amir Daneshmand, Francisco Facchinei, Vyacheslav Kungurtsev +1
We propose a decomposition framework for the parallel optimization of the sum of a differentiable {(possibly nonconvex)} function and a nonsmooth (possibly nonseparable), convex on…