Dr. Pavel Dvurechensky
| I am a member of the research group Stochastic Algorithms and Nonparametric Statistics of the Weierstrass Institute for Applied Analysis and Stochastics. |
Research interests
- Algorithms for large- and huge-scale optimization problems
- Optimization methods for problems with inexact oracle
- Numerical aspects of optimal transport distances and barycenters
- Stochastic optimization algorithms and randomized algorithms
- Algorithms for saddle-point problems and variational inequalities
- Distributed optimization
- Optimization beyond first-order methods
- Stochastic Optimization
- Optimal control of partial differential equations and nonlinear optimization
- Statistical inverse problems
Current projects
- MATH+-project EF3-3 Optimal transport for imaging
Joint project with M. Hintermüller, and V. Spokoiny.
Short CV
| Since 2015 | Research fellow, Research Group 6 "Stochastic Algorithms and Nonparametric Statistics", WIAS, Berlin |
| 2014 - 2015 | Research assistant, Institute for Information Transmission Problems, Moscow, Russia |
| 2009 - 2015 | Junior researcher, Moscow Institute of Physics and Technology, Moscow, Russia |
| 2013 | Ph.D., Moscow Institute of Physics and Technology, Moscow, Russia |
| 2010 | Master's Diploma, Moscow Institute of Physics and Technology, Moscow, Russia |
| 2008 | Bachelor's Diploma, Moscow Institute of Physics and Technology, Moscow, Russia |
| Extended CV |
Publications
Submitted Articles and Preprints
- F. Stonyakin, A. Gasnikov, A. Tyurin, D. Pasechnyuk, A. Agafonov, P. Dvurechensky, D. Dvinskikh, A. Kroshnin, V. Piskunova
Inexact model: A framework for optimization and variational inequalities.
arXiv:1902.00990 - S. Guminov, P. Dvurechensky, A. Gasnikov
On accelerated alternating minimization.
arXiv:1906.03622 - P. Dvurechensky, A. Gasnikov, E. Nurminsky, F. Stonyakin
Advances in low-memory subgradient optimization.
arXiv:1902.01572 - D. Dvinskikh, E. Gorbunov, A. Gasnikov, P. Dvurechensky, C.A. Uribe
On primal and dual approaches for distributed stochastic convex optimization over networks.
arXiv:1903.09844 - Y. Nesterov, A. Gasnikov, S. Guminov, P. Dvurechensky
Primal-dual accelerated gradient methods with small-dimensional relaxation oracle.
arXiv:1809.05895 - A. Ivanova, P. Dvurechensky, A. Gasnikov
Composite optimization for the resource allocation problem.
arXiv:1810.00595 - P. Dvurechensky, Y. Nesterov
Global performance guarantees of second-order methods for unconstrained convex minimization.
CORE Discussion Paper 2018/32, CORE UCL, 2018. pdf - P. Dvurechensky, A. Gasnikov, F. Stonyakin, A. Titov
Generalized Mirror Prox: Solving variational inequalities with monotone operator, inexact oracle, and unknown Hölder parameters.
arXiv:1806.05140 - P. Dvurechensky, A. Gasnikov, E. Gorbunov
An accelerated directional derivative method for smooth stochastic convex optimization.
arXiv:1804.02394 - P. Dvurechensky, A. Gasnikov, E. Gorbunov
An accelerated method for derivative-free smooth stochastic convex optimization.
arXiv:1802.09022 - P. Dvurechensky, A. Gasnikov, D. Kamzolov
Universal intermediate gradient method for convex problems with inexact oracle
arXiv:1712.06036 - P. Dvurechensky, A. Gasnikov, A. Tiurin
Randomized Similar Triangles Method: A Unifying Framework for Accelerated Randomized Optimization Methods (Coordinate Descent, Directional Search, Derivative-Free Method)
arXiv:1707.08486 - P. Dvurechensky, A. Gasnikov, S. Omelchenko, A. Tiurin
Adaptive Similar Triangles Method: a Stable Alternative to Sinkhorn's Algorithm for Regularized Optimal Transport
arXiv:1706.07622 - P. Dvurechensky
Gradient Method With Inexact Oracle for Composite Non-Convex Optimization
arXiv:1703.09180
Selected Refereed Articles
- F.S. Stonyakin, D. Dvinskikh, P. Dvurechensky, A. Kroshnin, O. Kuznetsova, A. Agafonov, A. Gasnikov, A. Tyurin, C. Uribe, D. Pasechnyuk, S. Artamonov
Gradient methods for problems with inexact model of the objective.
In Mathematical Optimization Theory and Operations Research (Cham, 2019), M. Khachay, Y. Kochetov, and P. Pardalos, Eds., Springer International Publishing, pp. 97-114. arXiv:1902.09001 - A. Kroshnin, N. Tupitsa, D. Dvinskikh, P. Dvurechensky, A. Gasnikov, C. Uribe
On the complexity of approximating Wasserstein barycenters.
In Proceedings of the 36th International Conference on Machine Learning (Long Beach, California, USA, 09-15 Jun 2019), K. Chaudhuri and R. Salakhutdinov, Eds., vol. 97 of Proceedings of Machine Learning Research, PMLR, pp. 3530-3540. arXiv:1901.08686 - S.V. Guminov, Y.E. Nesterov, A.V. Gasnikov, P.E. Dvurechensky, F.S. Stonyakin, A.A. Titov
Accelerated primal-dual gradient descent with linesearch for convex, nonconvex, and nonsmooth optimization problems.
Doklady Mathematics 99, 2 (Mar 2019), 125-128. - A.V. Gasnikov, P.E. Dvurechensky, F.S. Stonyakin, A.A. Titov
An adaptive proximal method for variational inequalities.
Computational Mathematics and Mathematical Physics 59, 5 (May 2019), 836-841. - A. Gasnikov, P. Dvurechensky, E. Gorbunov, E. Vorontsova, D. Selikhanovych, C.A. Uribe, B. Jiang, H. Wang, S. Zhang, S. Bubeck, Q. Jiang, Y. T. Lee, Y. Li, A. Sidford
Near optimal methods for minimizing convex functions with lipschitz p-th derivatives.
In Proceedings of the Thirty-Second Conference on Learning Theory (Phoenix, USA, 25-28 Jun 2019), A. Beygelzimer and D. Hsu, Eds., vol. 99 of Proceedings of Machine Learning Research, PMLR, pp. 1392-1393. arXiv:1809.00382 - D.R. Baimurzina, A.V. Gasnikov, E.V. Gasnikova, P.E. Dvurechensky, E.I. Ershov, M.B. Kubentaeva, A.A. Lagunovskaya
Universal method of searching for equilibria and stochastic equilibria in transportation networks.
Computational Mathematics and Mathematical Physics 59, 1 (2019), 19-33 arXiv:1701.02473 - C. A. Uribe, D. Dvinskikh, P. Dvurechensky, A. Gasnikov, A. Nedic
Distributed computation of Wasserstein barycenters over networks.
In 2018 IEEE Conference on Decision and Control (CDC) (2018), pp. 6544-6549 arXiv:1803.02933 - A. V. Gasnikov, P.E. Dvurechensky, M. E. Zhukovskii, S. V. Kim, S. S. Plaunov, D. A. Smirnov, F. A. Noskov
About the power law of the pagerank vector component distribution. Part 2. The Buckley-Osthus model, verification of the power law for this model, and setup of real search engines.
Numerical Analysis and Applications 11, 1 (2018), 16-32. - P. Dvurechensky, D. Dvinskikh, A. Gasnikov, C. A. Uribe, A. Nedic
Decentralize and randomize:Faster algorithm for Wasserstein barycenters.
In Advances in Neural Information Processing Systems 31 (2018), S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., NeurIPS 2018, Curran Associates, Inc., pp. 10783-10793. arXiv:1806.03915 - P. Dvurechensky, A. Gasnikov, A. Kroshnin
Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn's algorithm
In Proceedings of the 35th International Conference on Machine Learning (2018), J. Dy and A. Krause, Eds., vol. 80 of Proceedings of Machine Learning Research, pp. 1367-1376. arXiv:1802.04367 - P. E. Dvurechensky, A. V. Gasnikov, A. A. Lagunovskaya
Parallel algorithms and probability of large deviation for stochastic convex optimization problems.
Numerical Analysis and Applications 11, 1 (2018), 33-37. arXiv:1701.01830 - A. Bayandina, P. Dvurechensky, A. Gasnikov, F. Stonyakin, A. Titov
Mirror Descent and Convex Optimization Problems With Non-Smooth Inequality Constraints
In Large-Scale and Distributed Optimization, P. Giselsson and A. Rantzer, Eds. Springer International Publishing, 2018, ch. 8, pp. 181-215. arXiv:1710.06612 - A. V. Gasnikov, E. V. Gasnikova, P.E. Dvurechensky, A. A. M. Mohammed, E.O. Chernousova
About the power law of the pagerank vector component distribution. Part 1. Numerical methods for finding the pagerank vector.
Numerical Analysis and Applications 10, 4 (2017), 299-312. - A. S. Anikin, A. V. Gasnikov, P. E. Dvurechensky, A. I. Tyurin, and A. V. Chernov
Dual Approaches to the Minimization of Strongly Convex Functionals with a Simple Structure under Affine Constraints
Computational Mathematics and Mathematical Physics, 2017, V. 57, No. 8, pp. 1262-1276 - L. Bogolubsky, P. Dvurechensky, A. Gasnikov, G. Gusev, Yu. Nesterov, A. Raigorodskii, A. Tikhonov, M. Zhukovskii
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
In Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 4914-4922. arXiv:1603.00717 - P. Dvurechensky, A. Gasnikov
Stochastic Intermediate Gradient Method for Convex Problems with Inexact Stochastic Oracle
Journal of Optimization Theory and Applications, 2016 V. 171, No. 1, pp. 121-145, arXiv:1411.2876 - A. Chernov, P. Dvurechensky, A. Gasnikov
Fast Primal-Dual Gradient Method for Strongly Convex Minimization Problems with Linear Constraints
Kochetov, Yu. et all (eds.) Discrete Optimization and Operations Research. Proceedings of 9th International Conference,
DOOR 2016, Vladivostok, Russia, September 19-23, 2016. LNCS: Theoretical Computer Science and General Issues,
vol. 9869, pp. 391-403. Springer (2016), arXiv:1605.02970 - P. Dvurechensky, A. Gasnikov, E. Gasnikova, S. Matsievsky, A. Rodomanov, I. Usik
Primal-Dual Method for Searching Equilibrium in Hierarchical Congestion Population Games
Supplementary Proceedings of the 9th International Conference on Discrete Optimization and Operations Research
and Scientific School (DOOR 2016) Vladivostok, Russia, September 19 - 23, 2016. pp. 584-595 http://ceur-ws.org/Vol-1623/ - Gasnikov A.V., Dvurechensky P.E., Dorn Y.V., Maximov Y.V.
Numerical Methods for finding equilibrium flow distribution in Beckman and Stable Dynamics models
Matematicheskoe Modelirovanie, 2016, Vol. 28, No. 10, pp. 40-64 - A. V. Gasnikov, P. E. Dvurechensky
Stochastic Intermediate Gradient Method for Convex Optimization Problems
Doklady Mathematics, 2016, V. 93, No. 2, pp. 1-4 - P. Dvurechensky, Yu. Nesterov, V. Spokoiny
Primal-dual methods for solving infinite-dimensional games
Journal of Optimization Theory and Applications, 2015 V. 166, No. 1, pp. 23-51 - P.E. Dvurechensky, G.E. Ivanov
Algorithms for Computing Minkowski Operators and Their Application in Differential Games
Computational Mathematics and Mathematical Physics, 2014, V. 54, No. 2, pp. 235-264
Teaching
-
Recent developments in optimization methods and machine learning applications
Humboldt University, Berlin, winter semester 2019/2020
Contact details
| Pavel.Dvurechensky@wias-berlin.de | |
| Phone | +49 (0) 30 20372 465 |
| Fax | +49 (0) 30 20372 316 |
| Address | Room 212, Weierstrass Institute, Mohrenstrasse 39, 10117 Berlin, Germany | Researchgate profile | Pavel Dvurechensky |
Last updated 23.09.2019