Dr. Pavel Dvurechensky

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
My research is connected to the following Mathematical Research Topics of WIAS

Current projects

Short CV

Since 2015Research fellow, Research Group 6 "Stochastic Algorithms and Nonparametric Statistics", WIAS, Berlin
2014 - 2015Research assistant, Institute for Information Transmission Problems, Moscow, Russia
2009 - 2015Junior researcher, Moscow Institute of Physics and Technology, Moscow, Russia
2013Ph.D., Moscow Institute of Physics and Technology, Moscow, Russia
2010Master's Diploma, Moscow Institute of Physics and Technology, Moscow, Russia
2008Bachelor'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

Contact details

E-mail Pavel.Dvurechensky-please remove this text-@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
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Last updated 23.09.2019