Pavel E. Dvurechensky

According to our database, Pavel E. Dvurechensky authored at least 59 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Decentralized convex optimization on time-varying networks with application to Wasserstein barycenters.

Olga Yufereva, Michael Persiianov, Pavel E. Dvurechensky, Alexander V. Gasnikov, Dmitry Kovalev

Comput. Manag. Sci., June, 2024

Inexact tensor methods and their application to stochastic convex optimization.

Artem Agafonov, Dmitry Kamzolov, Pavel E. Dvurechensky, Alexander V. Gasnikov, Martin Takác

Optim. Methods Softw., 2024

Interaction-Force Transport Gradient Flows.

Egor Gladin, Pavel E. Dvurechensky, Alexander Mielke, Jia-Jie Zhu

CoRR, 2024

High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise.

Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Barrier Algorithms for Constrained Non-Convex Optimization.

Pavel E. Dvurechensky, Mathias Staudigl

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Analysis of Kernel Mirror Prox for Measure Optimization.

Pavel E. Dvurechensky, Jia-Jie Zhu

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact.

Nikita Kornilov, Alexander V. Gasnikov, Pavel E. Dvurechensky, Darina Dvinskikh

Comput. Manag. Sci., December, 2023

Accelerated gradient methods with absolute and relative noise in the gradient.

Artem Vasin, Alexander V. Gasnikov, Pavel E. Dvurechensky, Vladimir G. Spokoiny

Optim. Methods Softw., November, 2023

Generalized self-concordant analysis of Frank-Wolfe algorithms.

Pavel E. Dvurechensky, Kamil Safin, Shimrit Shtern, Mathias Staudigl

Math. Program., March, 2023

Algorithms for Euclidean-Regularised Optimal Transport.

Dmitry Pasechnyuk, Michael Persiianov, Pavel E. Dvurechensky, Alexander V. Gasnikov

Proceedings of the Optimization and Applications - 14th International Conference, 2023

High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance.

Abdurakhmon Sadiev, Marina Danilova, Eduard Gorbunov, Samuel Horváth, Gauthier Gidel, Pavel E. Dvurechensky, Alexander V. Gasnikov, Peter Richtárik

Proceedings of the International Conference on Machine Learning, 2023

2022

An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization.

Eduard Gorbunov, Pavel E. Dvurechensky, Alexander V. Gasnikov

SIAM J. Optim., 2022

Stochastic saddle-point optimization for the Wasserstein barycenter problem.

Daniil Tiapkin, Alexander V. Gasnikov, Pavel E. Dvurechensky

Optim. Lett., 2022

Zeroth-order methods for noisy Hölder-gradient functions.

Innokentiy Shibaev, Pavel E. Dvurechensky, Alexander V. Gasnikov

Optim. Lett., 2022

Generalized Mirror Prox Algorithm for Monotone Variational Inequalities: Universality and Inexact Oracle.

Fedor S. Stonyakin, Alexander V. Gasnikov, Pavel E. Dvurechensky, Alexander A. Titov, Mohammad S. Alkousa

J. Optim. Theory Appl., 2022

Oracle Complexity Separation in Convex Optimization.

Anastasiya Ivanova, Pavel E. Dvurechensky, Evgeniya A. Vorontsova, Dmitry Pasechnyuk, Alexander V. Gasnikov, Darina Dvinskikh, Alexander Tyurin

J. Optim. Theory Appl., 2022

Hyperfast second-order local solvers for efficient statistically preconditioned distributed optimization.

Pavel E. Dvurechensky, Dmitry Kamzolov, Aleksandr Lukashevich, Soomin Lee, Erik Ordentlich, César A. Uribe, Alexander V. Gasnikov

EURO J. Comput. Optim., 2022

Accelerated variance-reduced methods for saddle-point problems.

Ekaterina Borodich, Vladislav Tominin, Yaroslav Tominin, Dmitry Kovalev, Alexander V. Gasnikov, Pavel E. Dvurechensky

EURO J. Comput. Optim., 2022

A conditional gradient homotopy method with applications to Semidefinite Programming.

Pavel E. Dvurechensky, Shimrit Shtern, Mathias Staudigl

CoRR, 2022

Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise.

Eduard Gorbunov, Marina Danilova, David Dobre, Pavel E. Dvurechenskii, Alexander V. Gasnikov, Gauthier Gidel

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Decentralized Local Stochastic Extra-Gradient for Variational Inequalities.

Aleksandr Beznosikov, Pavel E. Dvurechensky, Anastasia Koloskova, Valentin Samokhin, Sebastian U. Stich, Alexander V. Gasnikov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The power of first-order smooth optimization for black-box non-smooth problems.

Alexander V. Gasnikov, Anton Novitskii, Vasilii Novitskii, Farshed Abdukhakimov, Dmitry Kamzolov, Aleksandr Beznosikov, Martin Takác, Pavel E. Dvurechensky, Bin Gu

Proceedings of the International Conference on Machine Learning, 2022

2021

Inexact model: a framework for optimization and variational inequalities.

Fedor S. Stonyakin, Alexander Tyurin, Alexander V. Gasnikov, Pavel E. Dvurechensky, Artem Agafonov, Darina Dvinskikh, Mohammad S. Alkousa, Dmitry Pasechnyuk, Sergei Artamonov, Victoria V. Piskunova

Optim. Methods Softw., 2021

Primal-dual accelerated gradient methods with small-dimensional relaxation oracle.

Yurii E. Nesterov, Alexander V. Gasnikov, Sergey Guminov, Pavel E. Dvurechensky

Optim. Methods Softw., 2021

Universal intermediate gradient method for convex problems with inexact oracle.

Dmitry Kamzolov, Pavel E. Dvurechensky, Alexander V. Gasnikov

Optim. Methods Softw., 2021

Composite optimization for the resource allocation problem.

Anastasiya Ivanova, Pavel E. Dvurechensky, Alexander V. Gasnikov, Dmitry Kamzolov

Optim. Methods Softw., 2021

An accelerated directional derivative method for smooth stochastic convex optimization.

Pavel E. Dvurechensky, Eduard Gorbunov, Alexander V. Gasnikov

Eur. J. Oper. Res., 2021

First-Order Methods for Convex Optimization.

Pavel E. Dvurechensky, Shimrit Shtern, Mathias Staudigl

EURO J. Comput. Optim., 2021

Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise.

Eduard Gorbunov, Marina Danilova, Innokentiy Shibaev, Pavel E. Dvurechensky, Alexander V. Gasnikov

CoRR, 2021

Decentralized Distributed Optimization for Saddle Point Problems.

Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Kovalev, Pavel E. Dvurechensky, Alexander V. Gasnikov

CoRR, 2021

On a Combination of Alternating Minimization and Nesterov's Momentum.

Sergey Guminov, Pavel E. Dvurechensky, Nazarii Tupitsa, Alexander V. Gasnikov

Proceedings of the 38th International Conference on Machine Learning, 2021

Newton Method over Networks is Fast up to the Statistical Precision.

Amir Daneshmand, Gesualdo Scutari, Pavel E. Dvurechensky, Alexander V. Gasnikov

Proceedings of the 38th International Conference on Machine Learning, 2021

An Accelerated Method For Decentralized Distributed Stochastic Optimization Over Time-Varying Graphs.

Alexander Rogozin, Mikhail Bochko, Pavel E. Dvurechensky, Alexander V. Gasnikov, Vladislav Lukoshkin

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

An Accelerated Second-Order Method for Distributed Stochastic Optimization.

Artem Agafonov, Pavel E. Dvurechensky, Gesualdo Scutari, Alexander V. Gasnikov, Dmitry Kamzolov, Aleksandr Lukashevich, Amir Daneshmand

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Recent Theoretical Advances in Non-Convex Optimization.

Marina Danilova, Pavel E. Dvurechensky, Alexander V. Gasnikov, Eduard Gorbunov, Sergey Guminov, Dmitry Kamzolov, Innokentiy Shibaev

CoRR, 2020

Zeroth-Order Algorithms for Smooth Saddle-Point Problems.

Abdurakhmon Sadiev, Aleksandr Beznosikov, Pavel E. Dvurechensky, Alexander V. Gasnikov

CoRR, 2020

Stochastic Saddle-Point Optimization for Wasserstein Barycenters.

Daniil Tiapkin, Alexander V. Gasnikov, Pavel E. Dvurechensky

CoRR, 2020

Optimal Combination of Tensor Optimization Methods.

Dmitry Kamzolov, Alexander V. Gasnikov, Pavel E. Dvurechensky

Proceedings of the Optimization and Applications - 11th International Conference, 2020

A Stable Alternative to Sinkhorn's Algorithm for Regularized Optimal Transport.

Pavel E. Dvurechensky, Alexander V. Gasnikov, Sergey Omelchenko, Alexander Tiurin

Proceedings of the Mathematical Optimization Theory and Operations Research, 2020

Self-Concordant Analysis of Frank-Wolfe Algorithms.

Pavel E. Dvurechensky, Petr Ostroukhov, Kamil Safin, Shimrit Shtern, Mathias Staudigl

Proceedings of the 37th International Conference on Machine Learning, 2020

Multimarginal Optimal Transport by Accelerated Alternating Minimization.

Nazarii Tupitsa, Pavel E. Dvurechensky, Alexander V. Gasnikov, César A. Uribe

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Generalized Self-concordant Hessian-barrier algorithms.

Pavel E. Dvurechensky, Mathias Staudigl, César A. Uribe

CoRR, 2019

Accelerated Alternating Minimization.

Sergey Guminov, Pavel E. Dvurechensky, Alexander V. Gasnikov

CoRR, 2019

On the Complexity of Approximating Wasserstein Barycenter.

Alexey Kroshnin, Darina Dvinskikh, Pavel E. Dvurechensky, Alexander V. Gasnikov, Nazarii Tupitsa, César A. Uribe

CoRR, 2019

Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator.

Evgeniya A. Vorontsova, Alexander V. Gasnikov, Eduard A. Gorbunov, Pavel E. Dvurechenskii

Autom. Remote. Control., 2019

Gradient Methods for Problems with Inexact Model of the Objective.

Fedor S. Stonyakin, Darina Dvinskikh, Pavel E. Dvurechensky, Alexey Kroshnin, Olesya Kuznetsova, Artem Agafonov, Alexander V. Gasnikov, Alexander Tyurin, César A. Uribe, Dmitry Pasechnyuk, Sergei Artamonov

Proceedings of the Mathematical Optimization Theory and Operations Research, 2019

On the Complexity of Approximating Wasserstein Barycenters.

Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel E. Dvurechensky, Alexander V. Gasnikov, Cesar A. Uribe

Proceedings of the 36th International Conference on Machine Learning, 2019

Optimal Tensor Methods in Smooth Convex and Uniformly ConvexOptimization.

Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe

Proceedings of the Conference on Learning Theory, 2019

Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives.

Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford

Proceedings of the Conference on Learning Theory, 2019

On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks.

Darina Dvinskikh, Eduard Gorbunov, Alexander V. Gasnikov, Pavel E. Dvurechensky, César A. Uribe

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters.

Pavel E. Dvurechenskii, Darina Dvinskikh, Alexander V. Gasnikov, César A. Uribe, Angelia Nedich

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm.

Pavel E. Dvurechensky, Alexander V. Gasnikov, Alexey Kroshnin

Proceedings of the 35th International Conference on Machine Learning, 2018

Distributed Computation of Wasserstein Barycenters Over Networks.

Cesar A. Uribe, Darina Dvinskikh, Pavel E. Dvurechensky, Alexander V. Gasnikov, Angelia Nedic

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Randomized Similar Triangles Method: A Unifying Framework for Accelerated Randomized Optimization Methods (Coordinate Descent, Directional Search, Derivative-Free Method).

Pavel E. Dvurechensky, Alexander V. Gasnikov, Alexander Tiurin

CoRR, 2017

2016

Stochastic Intermediate Gradient Method for Convex Problems with Stochastic Inexact Oracle.

Pavel E. Dvurechensky, Alexander V. Gasnikov

J. Optim. Theory Appl., 2016

Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods.

Lev Bogolubsky, Pavel E. Dvurechensky, Alexander V. Gasnikov, Gleb Gusev, Yurii E. Nesterov, Andrei M. Raigorodskii, Aleksey Tikhonov, Maksim Zhukovskii

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Primal-Dual Method for Searching Equilibrium in Hierarchical Congestion Population Games.

Pavel E. Dvurechensky, Alexander V. Gasnikov, Evgenia Gasnikova, Sergey Matsievsky, Anton Rodomanov, Inna Usik

Proceedings of the Supplementary Proceedings of the 9th International Conference on Discrete Optimization and Operations Research and Scientific School (DOOR 2016), Vladivostok, Russia, September 19, 2016

Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints.

Alexey Chernov, Pavel E. Dvurechensky, Alexander V. Gasnikov

Proceedings of the Discrete Optimization and Operations Research, 2016

2015

Primal-Dual Methods for Solving Infinite-Dimensional Games.

Pavel E. Dvurechensky, Yurii E. Nesterov, Vladimir G. Spokoiny

J. Optim. Theory Appl., 2015