12 citations · 25 across the 5 of their papers we have counts for
7 papers
Stochastic Gradient Descent with Preconditioned Polyak Step-size
Farshed Abdukhakimov, Chulu Xiang, Dmitry Kamzolov +1
Stochastic Gradient Descent (SGD) is one of the many iterative optimization methods that are widely used in solving machine learning problems. These methods display valuable proper…
Stochastic Gradient Methods with Preconditioned Updates
Abdurakhmon Sadiev, Aleksandr Beznosikov, Abdulla Jasem Almansoori +3
This work considers the non-convex finite sum minimization problem. There are several algorithms for such problems, but existing methods often work poorly when the problem is badly…
The Power of First-Order Smooth Optimization for Black-Box Non-Smooth Problems
Alexander Gasnikov, Anton Novitskii, Vasilii Novitskii +6
Gradient-free/zeroth-order methods for black-box convex optimization have been extensively studied in the last decade with the main focus on oracle calls complexity. In this paper,…
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…
Near-Optimal Hyperfast Second-Order Method for convex optimization and its Sliding
Dmitry Kamzolov, Alexander Gasnikov
In this paper, we present a new Hyperfast Second-Order Method with convergence rate up to a logarithmic factor for the convex function with Lipshitz the third derivativ…
Composite optimization for the resource allocation problem
Anastasiya Ivanova, Pavel Dvurechensky, Alexander Gasnikov +1
In this paper we consider resource allocation problem stated as a convex minimization problem with linear constraints. To solve this problem, we use gradient and accelerated gradie…