5 papers
FLECS-CGD: A Federated Learning Second-Order Framework via Compression and Sketching with Compressed Gradient Differences
Artem Agafonov, Brahim Erraji, Martin Takáč
In the recent paper FLECS (Agafonov et al, FLECS: A Federated Learning Second-Order Framework via Compression and Sketching), the second-order framework FLECS was proposed for the…
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…
Lower bounds for conditional gradient type methods for minimizing smooth strongly convex functions
Artem Agafonov
In this paper, we consider conditional gradient methods. These are methods that use a linear minimization oracle, which, for a given vector , computes the solut…
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…
Inexact Model: A Framework for Optimization and Variational Inequalities
Fedor Stonyakin, Alexander Gasnikov, Alexander Tyurin +6
In this paper we propose a general algorithmic framework for first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems and variat…