Pavel Dvurechensky

Publications³⁷

• Article Tupitsa N., Dvurechensky P., Gasnikov A., Гуминов С. В. Alternating minimization methods for strongly convex optimization // Journal of Inverse and Ill-posed problems. 2021. Vol. 29. No. 5. P. 721-739. doi
• Article Dvurechensky P., Eduard Gorbunov, Gasnikov A. An accelerated directional derivative method for smooth stochastic convex optimization // European Journal of Operational Research. 2021. Vol. 290. No. 2. P. 601-621. doi
• Article Стонякин Ф. С., Tyurin A., Gasnikov A., Dvurechensky P., Агафонов А. Д., Двинских Д. М., Alkousa M., Пасечнюк Д. А., Artamonov S., Пискунова В. Inexact model: a framework for optimization and variational inequalities // Optimization Methods and Software. 2021 doi
• Preprint Gorbunov E., Danilova M., Shibaev I., Dvurechensky P., Gasnikov A. Near-Optimal High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise / arXiv. Series arXiv:2106.05958 "arXiv:2106.05958". 2021.
• Chapter Daneshmand A., Scutari G., Dvurechensky P., Gasnikov A. Newton Method over Networks is Fast up to the Statistical Precision, in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021) Vol. 139. PMLR, 2021. Ch. 139. P. 2398-2409.
• Article Воронцова Е. А., Gasnikov A., Dvurechensky P., Ivanova A., Пасечнюк Д. А. Numerical Methods for the Resource Allocation Problem in a Computer Network // Computational Mathematics and Mathematical Physics. 2021. Vol. 61. No. 2. P. 297-328. doi
• Article Гуминов С. В., Dvurechensky P., Tupitsa N., Gasnikov A. On a Combination of Alternating Minimization and Nesterov’s Momentum // Proceedings of Machine Learning Research. 2021. Vol. 139. P. 3886-3898.
• Article Gladin E., Sadiev A., Gasnikov A., Dvurechensky P., Beznosikov A., Alkousa M. Solving Smooth Min-Min and Min-Max Problems by Mixed Oracle Algorithms // Communications in Computer and Information Science. 2021. Vol. 1476. P. 19-40. doi
• Article Sadiev A., Beznosikov A., Dvurechensky P., Gasnikov A. Zeroth-Order Algorithms for Smooth Saddle-Point Problems // Communications in Computer and Information Science. 2021. Vol. 1476. P. 71-85. doi
• Article Shibaev I., Dvurechensky P., Gasnikov A. Zeroth-order methods for noisy Hölder-gradient functions // Optimization Letters. 2021 doi
• Chapter Dvurechensky P., Gasnikov A., Omelchenko S., Tyurin A. A Stable Alternative to Sinkhorn’s Algorithm for Regularized Optimal Transport, in: Mathematical Optimization Theory and Operations Research, 19th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, (Т. 12095) / Ed. by A. Kononov, M. Khachay, P. Pardalos, V. A. Kalyagin. Cham : Springer, 2020. doi P. 406-423. doi
• Chapter Dvurechensky P., Gasnikov A., Nurminski E., Stonyakin F. Advances in Low-Memory Subgradient Optimization, in: Numerical Nonsmooth Optimization. Springer, 2020. doi P. 19-59. doi
• Article Ivanova A., Dvurechensky P., Gasnikov A., Kamzolov D. Composite optimization for the resource allocation problem // Optimization Methods and Software. 2020. P. 1-35. doi (in press)
• Chapter Tupitsa, N., Dvurechensky P., Gasnikov A., Uribe C. A. Multimarginal Optimal Transport by Accelerated Alternating Minimization, in: 2020 IEEE 59th Conference on Decision and Control (CDC). IEEE, 2020. doi P. 6132-6137. doi
• Preprint Dvurechensky P., Gasnikov A., Остроухов П., Uribe C., Ivanova A. Near-optimal tensor methods for minimizing the gradient norm of convex function / Working papers by Cornell University.. Series - "Optimization and Control". 2020. (in press)
• Preprint Ivanova A., Gasnikov A., Dvurechensky P., Двинских Д., Тюрин А. И., Воронцова Е., Пасечнюк Д. Oracle Complexity Separation in Convex Optimization / Working papers by Cornell University.. Series - "Optimization and Control". 2020. (in press)
• Article Gasnikov A., Dvurechensky P. Primal–dual accelerated gradient methods with small-dimensional relaxation oracle // Optimization Methods and Software. 2020. P. 1-38. doi (in press)
• Preprint Danilova M., Dvurechensky P., Gasnikov A., Gorbunov E., Sergey Guminov, Kamzolov D., Shibaev I. Recent Theoretical Advances in Non-Convex Optimization / arXiv. Series arXiv:2012.06188 "arXiv:2012.06188". 2020.
• Chapter Dvurechensky P., Ostroukhov P., Safin K., Shtern S., Staudigl M. Self-concordant analysis of Frank-Wolfe algorithms, in: International Conference on Machine Learning (ICML 2020) Vol. 119. PMLR, 2020. (in press)
• Chapter Tupitsa N., Gasnikov A., Dvurechensky P., Guminov S. Strongly Convex Optimization for the Dual Formulation of Optimal Transport, in: Mathematical Optimization Theory and Operations Research. MOTOR 2020. Communications in Computer and Information Science Vol. 1275. Springer, 2020. doi P. 192-204. doi
• Article Gasnikov A., Dvurechensky P. Universal intermediate gradient method for convex problems with inexact oracle // Optimization Methods and Software. 2020. P. 1-28. doi (in press)
• Article Guminov S., Nesterov Y., Dvurechensky P., Gasnikov A. Accelerated primal-dual gradient descent with linesearch for convex, nonconvex, and nonsmooth optimization problems / Пер. с рус. // Doklady Mathematics. 2019. Vol. 99. No. 2. P. 125-128. doi
• Article Gasnikov A., Dvurechensky P., Stonyakin F. S., Titov A. An Adaptive Proximal Method for Variational Inequalities // Computational Mathematics and Mathematical Physics. 2019. Vol. 59. P. 836-841. doi
• Chapter Stonyakin F., Dvinskikh D., Dvurechensky P., Kroshnin A., Kuznetsova O., Agafonov A., Gasnikov A., Tyurin A., Uribe C., Pasechnyuk D., Artamonov S. Gradient Methods for Problems with Inexact Model of the Objective, in: Mathematical Optimization Theory and Operations Research, 18th International Conference, MOTOR 2019 Ekaterinburg, Russia, July 8–12, 2019 / Ed. by М. Ю. Хачай, Ю. А. Кочетов, P. M. Pardalos. Vol. 11548. Springer, 2019. P. 97-114. doi
• Chapter Gasnikov A., Gorbunov E., Dvurechensky P., Vorontsova E., Selikhanovich D., Uribe C., Jiang B., Haoyue W. Near Optimal Methods for Minimizing Convex Functions with Lipschitz p-th Derivatives, in: Proceedings of Machine Learning Research Vol. 99: Conference on Learning Theory, 25-28 June 2019, Phoenix, AZ, USA. PMLR, 2019.. PMLR, 2019. P. 1392-1393.
• Preprint Иванова А. С., Пасечнюк Д., Двуреченский П. Е., Гасников А. В., Воронцова Е. Numerical methods for the resource allocation problem in networks / Cornell University. Серия "Working papers by Cornell University". 2019. (in press)
• Chapter Dvinskikh D., Gorbunov E., Gasnikov A., Dvurechensky P., Uribe C. On Primal and Dual Approaches for Distributed Stochastic Convex Optimization over Networks, in: 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. doi P. 7435-7440. doi
• Chapter Kroshnin A., Dvinskikh D., Dvurechensky P., Gasnikov Alexander, Tupitsa Nazarii, Uribe C. A. On the Complexity of Approximating Wasserstein Barycenters, in: Proceedings of Machine Learning Research Vol. 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA. PMLR, 2019. P. 3530-3540.
• Chapter Gasnikov A., Dvurechensky P., Gorbunov E., Vorontsova E., Selikhanovych D., Uribe C. Optimal Tensor Methods in Smooth Convex and Uniformly Convex Optimization, in: Conference on Learning Theory, 25-28 June 2019, Phoenix, USA Vol. 99. , 2019. P. 1374-1391.
• Article Baimurzina D., Gasnikov A., Dvurechensky P., Ershov E., Kubentaeva M., Lagunovskaya A. Universal Method of Searching for Equilibria and Stochastic Equilibria in Transportation Networks // Computational Mathematics and Mathematical Physics. 2019. Vol. 59. No. 1. P. 19-33. doi
• Article Воронцова Е. А., Гасников А. В., Горбунов Э. А., Двуреченский П. Е. Ускоренные безградиентные методы оптимизации с неевклидовым проксимальным оператором // Автоматика и телемеханика. 2019. № 8. С. 149-168. doi
• Article Гасников А. В., Двуреченский П. Е., Жуковский М. Е., Ким С. В., Плаунов С. С., Смирнов Д. А., Носков Ф. А. 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 // Сибирский журнал вычислительной математики. 2018. Т. 21. № 1. С. 23-45. doi
• Chapter Dvurechensky P., Gasnikov A., Kroshnin A. 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) Vol. 80. PMLR, 2018. P. 1367-1376.
• Chapter Dvurechensky P., Dvinskikh D., Gasnikov A., Uribe C., Nedic A. Decentralize and randomize: Faster algorithm for Wasserstein barycenters, in: Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Neural Information Processing Systems Foundation, 2018. P. 10760-10770.
• Chapter Bayandina A., Dvurechensky P., Gasnikov A., Stonyakin F. S., Titov A. Mirror Descent and Convex Optimization Problems with Non-smooth Inequality Constraints, in: Large-Scale and Distributed Optimization Vol. 2227. Springer, 2018. doi P. 181-213. doi
• Chapter Bogolubsky L., Dvurechensky P., Gasnikov A., Gusev G., Nesterov Y., Raigorodsky A., Tikhonov A., Zhukovskii M. Learning supervised pagerank with gradient-based and gradient-free optimization methods, in: Advances in Neural Information Processing Systems 29 (NIPS 2016). NY : Curran Associates, 2016. P. 4914-4922.
• Article Dvurechensky P., Gasnikov A. Stochastic intermediate gradient method for convex problems with stochastic inexact oracle // Journal of Optimization Theory and Applications. 2016. Vol. 171. No. 1. P. 121-145. doi