19 citations · 42 across the 3 of their papers we have counts for
6 papers
Randomized Progressive Hedging methods for Multi-stage Stochastic Programming
Gilles Bareilles, Yassine Laguel, Dmitry Grishchenko +2
Progressive Hedging is a popular decomposition algorithm for solving multi-stage stochastic optimization problems. A computational bottleneck of this algorithm is that all scenario…
Proximal Gradient methods with Adaptive Subspace Sampling
Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick
Many applications in machine learning or signal processing involve nonsmooth optimization problems. This nonsmoothness brings a low-dimensional structure to the optimal solutions.…
Adaptive Catalyst for Smooth Convex Optimization
Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko +3
In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization probl…
A Privacy Preserving Randomized Gossip Algorithm via Controlled Noise Insertion
Filip Hanzely, Jakub Konečný, Nicolas Loizou +2
In this work we present a randomized gossip algorithm for solving the average consensus problem while at the same time protecting the information about the initial private values s…
Distributed Learning with Sparse Communications by Identification
Dmitry Grishchenko, Franck Iutzeler, Jérôme Malick +1
In distributed optimization for large-scale learning, a major performance limitation comes from the communications between the different entities. When computations are performed b…
Privacy Preserving Randomized Gossip Algorithms
Filip Hanzely, Jakub Konečný, Nicolas Loizou +2
In this work we present three different randomized gossip algorithms for solving the average consensus problem while at the same time protecting the information about the initial p…