66 citations · 525 across the 94 of their papers we have counts for
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Adaptive Algorithms for Relatively Lipschitz Continuous Convex Optimization Problems
Fedor Stonyakin, Alexander Titov, Mohammad Alkousa +2
Recently there were proposed some innovative convex optimization concepts, namely, relative smoothness [1] and relative strong convexity [2,3]. These approaches have significantly…
Stochastic optimization for dynamic pricing
Dmitry Pasechnyuk, Pavel Dvurechensky, Sergey Omelchenko +1
We consider the problem of supply and demand balancing that is stated as a minimization problem for the total expected revenue function describing the behavior of both consumers an…
High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
Eduard Gorbunov, Marina Danilova, Innokentiy Shibaev +2
Stochastic first-order methods are standard for training large-scale machine learning models. Random behavior may cause a particular run of an algorithm to result in a highly subop…
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…
An Accelerated Method For Decentralized Distributed Stochastic Optimization Over Time-Varying Graphs
Alexander Rogozin, Mikhail Bochko, Pavel Dvurechensky +2
We consider a distributed stochastic optimization problem that is solved by a decentralized network of agents with only local communication between neighboring agents. The goal of…
Algorithms for solving variational inequalities and saddle point problems with some generalizations of Lipschitz property for operators
Alexander Titov, Fedor Stonyakin, Mohammad Alkousa +1
The article is devoted to the development of numerical methods for solving saddle point problems and variational inequalities with simplified requirements for the smoothness condit…
Hyperfast Second-Order Local Solvers for Efficient Statistically Preconditioned Distributed Optimization
Pavel Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich +4
Statistical preconditioning enables fast methods for distributed large-scale empirical risk minimization problems. In this approach, multiple worker nodes compute gradients in para…
Accelerated gradient methods with absolute and relative noise in the gradient
Vasin Artem, Alexander Gasnikov, Pavel Dvurechensky +1
In this paper, we investigate accelerated first-order methods for smooth convex optimization problems under inexact information on the gradient of the objective. The noise in the g…
On solving convex min-min problems with smoothness and strong convexity in one variable group and small dimension of the other
Egor Gladin, Mohammad Alkousa, Alexander Gasnikov
This paper is devoted to some approaches for convex min-min problems with smoothness and strong convexity in only one of the two variable groups. It is shown that the proposed appr…