74 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- A. Gasnikov4 profiles16 · h 31
- A. Beznosikov2 profiles11 · h 18
- A. M. Raigorodskii2 profiles6 · h 7
- D. Musatov5 · h 9
- K. Alfaro-Bittner2 profiles5 · h 8
- K. Kovalenko2 profiles5 · h 5
- S. Boccaletti5 · h 60
- A. Rogozin4 · h 13
- Eduard A. Gorbunov4 · h 29
- M. Perc4 · h 119
- A. Polyanskii3 · h 8
- Abdurakhmon Sadiev2 · h 11
- Moscow Institute of Physics and TechnologyRU31 papers
- Institute for Information Transmission ProblemsRU11 papers
- National Research University Higher School of EconomicsRU8 papers
- Buryat State UniversityRU7 papers
- Lomonosov Moscow State UniversityRU7 papers
- The Russian Presidential Academy of National Economy and Public AdministrationRU7 papers
- Institute for Complex SystemsIT6 papers
- Universidad Rey Juan CarlosES6 papers
- Alma Mater EuropaeaSI4 papers
- China Medical UniversityTW4 papers
- China Medical University HospitalTW4 papers
- Complexity Science HubAT4 papers
6 papers · 2 filters
Randomized gradient-free methods in convex optimization
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky +3
This review presents modern gradient-free methods to solve convex optimization problems. By gradient-free methods, we mean those that use only (noisy) realizations of the objective…
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities
Aleksandr Beznosikov, Alexander Gasnikov
Variational inequalities are a broad formalism that encompasses a vast number of applications. Motivated by applications in machine learning and beyond, stochastic methods are of g…
Decentralized convex optimization over time-varying graphs: a survey
Alexander Rogozin, Alexander Gasnikov, Aleksander Beznosikov +1
Decentralized optimization over time-varying networks has a wide range of applications in distributed learning, signal processing and various distributed control problems. The agen…
Decentralized Strongly-Convex Optimization with Affine Constraints: Primal and Dual Approaches
Alexander Rogozin, Demyan Yarmoshik, Ksenia Kopylova +1
Decentralized optimization is a common paradigm used in distributed signal processing and sensing as well as privacy-preserving and large-scale machine learning. It is assumed that…
Compression and Data Similarity: Combination of Two Techniques for Communication-Efficient Solving of Distributed Variational Inequalities
Aleksandr Beznosikov, Alexander Gasnikov
Variational inequalities are an important tool, which includes minimization, saddles, games, fixed-point problems. Modern large-scale and computationally expensive practical applic…
A Unified Analysis of Variational Inequality Methods: Variance Reduction, Sampling, Quantization and Coordinate Descent
Aleksandr Beznosikov, Alexander Gasnikov, Karina Zainulina +2
In this paper, we present a unified analysis of methods for such a wide class of problems as variational inequalities, which includes minimization problems and saddle point problem…