5 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…