Aleksandr Gasnikov

Publications⁹⁴

• Article Kubentayeva M., Yarmoshik D., Persiianov M., Kroshnin Alexey, Kotliarova E., Tupitsa N., Pasechnyuk D., Gasnikov Alexander, Shvetsov V., Baryshev L., Shurupov A. Primal-dual gradient methods for searching network equilibria in combined models with nested choice structure and capacity constraints // Computational Management Science. 2024. Vol. 21. Article 15. doi
• Chapter Kornilov N., Shamir O., Lobanov A., Dvinskikh D., Alexander Gasnikov, Shibaev I., Gorbunov E., Horváth S. Accelerated zeroth-order method for non-smooth stochastic convex optimization problem with infinite variance, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023.
• Article Rogozin A., Beznosikov A., Dvinskikh D., Kovalev D., Dvurechensky P., Gasnikov A. Decentralized saddle point problems via non-Euclidean mirror prox // Optimization Methods and Software. 2023. P. 1-26. doi
• Chapter Beznosikov A., Samsonov S., Sheshukova M., Gasnikov A., Naumov A., Moulines E. First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities, in: Advances in Neural Information Processing Systems 36 (NeurIPS 2023). Curran Associates, Inc., 2023. P. 44820-44835.
• Article Alashqar B., Gasnikov A., Dvinskikh D., Lobanov A. Gradient-free Federated Learning Methods with l1 and l2-randomization for Non-smooth Convex Stochastic Optimization Problems // Computational Mathematics and Mathematical Physics. 2023. P. 1600-1653.
• Article Gorbunov E., Dvurechensky P., Gasnikov A. An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization // SIAM Journal on Optimization. 2022. Vol. 32. No. 2. P. 1210-1238. doi
• Article Sadiev A., Borodich E., Beznosikov A., Dvinskikh D., Chezhegov S., Tappenden R., Takáč M., Alexander Gasnikov. Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes // EURO Journal on Computational Optimization. 2022. Vol. 10. Article 100041. doi
• Chapter Beznosikov A., Richtarik P., Diskin M., Ryabinin M., Alexander Gasnikov. Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees, in: Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022. Curran Associates, Inc., 2022. P. 14013-14029.
• Article Anikin A., Gasnikov A., Gornov A., Kamzolov D., Maximov Y., Nesterov Y. Efficient numerical methods to solve sparse linear equations with application to PageRank // Optimization Methods and Software. 2022. Vol. 37. No. 3. P. 907-935. doi
• Article Stonyakin F., Gasnikov A., Dvurechensky P., Titov A., Alkousa M. Generalized Mirror Prox Algorithm for Monotone Variational Inequalities: Universality and Inexact Oracle // Journal of Optimization Theory and Applications. 2022. Vol. 194. No. 3. P. 988-1013. doi
• Chapter Dvinskikh D., Tominin V., Tominin I., Gasnikov Alexander. Noisy Zeroth-Order Optimization for Non-smooth Saddle Point Problems, in: Mathematical Optimization Theory and Operations Research, 21st International Conference, MOTOR 2022, Petrozavodsk, Russia, July 2–6, 2022, Proceedings Vol. 13367. Cham: Springer, 2022. doi Ch. 279899. P. 18-33. doi
• Article Ivanova A., Dvurechensky P., Vorontsova E., Pasechnyuk D., Gasnikov A., Dvinskikh D., Tyurin A. Oracle Complexity Separation in Convex Optimization // Journal of Optimization Theory and Applications. 2022. Vol. 193. No. 1-3. P. 462-490. doi
• Chapter Tiapkin D., Alexander Gasnikov. Primal-Dual Stochastic Mirror Descent for MDPs, in: International Conference on Artificial Intelligence and Statistics, 28-30 March 2022, A Virtual Conference Vol. 151: Proceedings of The 25th International Conference on Artificial Intelligence and Statistics. PMLR, 2022. P. 9723-9740.
• Chapter Gorbunov E., Rogozin A., Beznosikov A., Dvinskikh D., Gasnikov A. Recent Theoretical Advances in Decentralized Distributed Convex Optimization, in: High-Dimensional Optimization and Probability: With a View Towards Data Science. Springer, 2022. Ch. 191. P. 253-325. doi
• Article Tiapkin D., Gasnikov A., Dvurechensky P. Stochastic saddle-point optimization for the Wasserstein barycenter problem // Optimization Letters. 2022. Vol. 16. No. 7. P. 2145-2175. doi
• Article Shibaev I., Dvurechensky P., Gasnikov A. Zeroth-order methods for noisy Hölder-gradient functions // Optimization Letters. 2022. Vol. 16. No. 7. P. 2123-2143. doi
• Chapter Kovalev D., Shulgin E., Richtarik P., Rogozin A., Gasnikov A. ADOM: Accelerated Decentralized Optimization Method for Time-Varying Networks, in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021) Vol. 139. PMLR, 2021. P. 5784-5793.
• Chapter Ivanova A., Pasechnyuk D., Grishchenko D., Shulgin E., Gasnikov A., Matyukhin V. Adaptive Catalyst for Smooth Convex Optimization, in: Optimization and Applications: 12th International Conference, OPTIMA 2021, Petrovac, Montenegro, September 27 – October 1, 2021, Proceedings. Switzerland : Springer, 2021. doi Ch. 268319. P. 20-37. doi
• Chapter Titov A., Stonyakin F. S., Alkousa M., Gasnikov A. Algorithms for Solving Variational Inequalities and Saddle Point Problems with Some Generalizations of Lipschitz Property for Operators, in: Mathematical Optimization Theory and Operations Research: Recent Trends: 20th International Conference, MOTOR 2021, Irkutsk, Russia, July 5–10, 2021, Revised Selected Papers. Cham : Springer, 2021. doi Ch. 6. P. 86-101. doi
• 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 Ivanova A., Dvurechensky P., Gasnikov A., Kamzolov D. Composite optimization for the resource allocation problem // Optimization Methods and Software. 2021. Vol. 36. No. 4. P. 720-754. doi
• Article Dvinskikh D., Gasnikov A. Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems // Journal of Inverse and Ill-posed problems. 2021. Vol. 29. No. 3. P. 385-405. doi
• Article Beznosikov A., Gorbunov E., Gasnikov A. Derivative-Free Method For Composite Optimization With Applications To Decentralized Distributed Optimization // IFAC-PapersOnLine. 2021. Vol. 53. No. 2. P. 4038-4043. doi
• Article Kubentayeva M., Gasnikov A. Finding equilibria in the traffic assignment problem with primal-dual gradient methods for stable dynamics model and beckmann model // Mathematics. 2021. Vol. 9. No. 11. Article 1217. doi
• Article Stonyakin F., Tyurin A., Gasnikov A., Dvurechensky P., Agafonov A., Dvinskikh D., Alkousa M., Pasechnyuk D., Artamonov S., Piskunova V. Inexact model: a framework for optimization and variational inequalities // Optimization Methods and Software. 2021. Vol. 36. No. 6. P. 1155-1201. 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
• Chapter Guminov S., Dvurechensky P., Tupitsa N., Gasnikov A. On a Combination of Alternating Minimization and Nesterov’s Momentum, in: Proceedings of the 38th International Conference on Machine Learning (ICML 2021) Vol. 139. PMLR, 2021. P. 3886-3898.
• Chapter Beznosikov A., Novitskii V., Gasnikov A. One-Point Gradient-Free Methods for Smooth and Non-smooth Saddle-Point Problems, in: Mathematical Optimization Theory and Operations Research: 20th International Conference, MOTOR 2021, Irkutsk, Russia, July 5–10, 2021, Proceedings / Ed. by P. M. Pardalos, M. Y. Khachay, A. Kazakov. Cham : Springer, 2021. doi Ch. 261179. P. 144-158. doi
• Article Nesterov Y., Gasnikov A., Guminov S., Dvurechensky P. Primal–dual accelerated gradient methods with small-dimensional relaxation oracle // Optimization Methods and Software. 2021. Vol. 36. No. 4. P. 773-810. doi
• 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 Kamzolov D., Dvurechensky P., Gasnikov A. Universal intermediate gradient method for convex problems with inexact oracle // Optimization Methods and Software. 2021. Vol. 36. No. 6. P. 1289-1316. 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 Котлярова Е. В., Гасников А. В., Гасникова Е. В., Ярмошки Д. В. Поиск равновесий в двухстадийных моделях распределения транспортных потоков по сети / Пер. с англ. // Компьютерные исследования и моделирование. 2021. Т. 2. № 13. С. 365-379. 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
• Article Dvinskikh D., Омельченко С. С., Gasnikov A., Tyurin A. Accelerated Gradient Sliding for Minimizing a Sum of Functions // Doklady Mathematics. 2020. Vol. 101. No. 3. P. 244-246. doi
• Article Alkousa M., Gasnikov A., Dvinskikh D., Kovalev D., Stonyakin F. S. Accelerated Methods for Saddle-Point Problem // Computational Mathematics and Mathematical Physics. 2020. Vol. 60. No. 11. P. 1787-1809. doi
• Article Dvinskikh D., Tyurin A., Gasnikov A., Omel’chenko C. Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality // Mathematical notes. 2020. Vol. 108. No. 3-4. P. 511-522. 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
• Chapter Titov A., Stonyakin F. S., Alkousa M., Ablaev S. A., Gasnikov A. Analogues of Switching Subgradient Schemes for Relatively Lipschitz-Continuous Convex Programming Problems, in: Mathematical Optimization Theory and Operations Research. 19th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, Revised Selected Papers Vol. 1275: Communications in Computer and Information Science . Springer, 2020. P. 133-149. doi
• Article Beznosikov A., Sadiev A., Gasnikov A. Gradient-Free Methods with Inexact Oracle for Convex-Concave Stochastic Saddle-Point Problem // Communications in Computer and Information Science. 2020. Vol. 1275. P. 105-119. doi
• Article Stonyakin F. S., Stepanov A. N., Gasnikov A., Titov A. Mirror descent for constrained optimization problems with large subgradient values of functional constraints // Computer Research and Modeling. 2020. Vol. 12. No. 2. P. 301-317. doi
• 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)
• Article Rogozin A., Uribe C., Gasnikov A., Malkovsky N., Nedić A. Optimal distributed convex optimization on slowly time-varying graphs // IEEE Transactions on Control of Network Systems. 2020. Vol. 7. No. 2. P. 829-841. doi
• Preprint Ivanova A., Gasnikov A., Dvurechensky P., Тюрин А. И., Воронцова Е., Пасечнюк Д., Dvinskikh D. Oracle Complexity Separation in Convex Optimization / Working papers by Cornell University.. Series - "Optimization and Control". 2020. (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 Gorbunov E., Danilova M., Gasnikov A. Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping, in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Curran Associates, Inc., 2020. P. 15042-15053.
• 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
• Preprint Rogozin A., Lukoshkin V., Gasnikov A., Kovalev D., Shulgin E. Towards accelerated rates for distributed optimization over time-varying networks / Cornell University. Series arXiv "math". 2020.
• Article Vorontsova E., Gasnikov A., Dvurechensky P., Gorbunov E. Accelerated Gradient-Free Optimization Methods with a Non-Euclidean Proximal Operator / Пер. с рус. // Automation and Remote Control. 2019. Vol. 80. No. 8. P. 1487-1501. doi
• 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
• Preprint Ivanova A., Стонякин Ф., Пасечнюк Д., Воронцова Е., Gasnikov A. Adaptive Mirror Descent for the Network Utility Maximization Problem / Working papers by Cornell University.. Series - "Optimization and Control". 2019. (in press)
• 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
• Article Gasnikov A., Tyurin A. Fast Gradient Descent for Convex Minimization Problems with an Oracle Producing a (δ, L)-Model of Function at the Requested Point / Пер. с рус. // Computational Mathematics and Mathematical Physics. 2019. Vol. 59. No. 7. P. 1085-1097. 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 Titov A., Stonyakin F. S., Gasnikov A., Alkousa M. Mirror Descent and Constrained Online Optimization Problems, in: Optimization and Applications 9th International Conference, OPTIMA 2018, Petrovac, Montenegro, October 1–5, 2018, Revised Selected Papers / Ed. by M. Jaćimović, M. Khachay, Y. Kochetov, V. Malkova, Ю. Г. Евтушенко, M. Posypkin. Springer, 2019. doi P. 64-78. 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., Tupitsa Nazarii, Dvinskikh D., Dvurechensky P., Gasnikov Alexander, 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. Optimal Tensor Methods in Smooth Convex and Uniformly Convex Optimization, in: Proceedings of Machine Learning Research Vol. 99: Conference on Learning Theory, 25-28 June 2019, Phoenix, AZ, USA. PMLR, 2019.. PMLR, 2019. (in press)
• 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.
• Book Gasnikov A. Proceedings of Machine Learning Research Vol. 99: Conference on Learning Theory, 25-28 June 2019, Phoenix, AZ, USA. PMLR, 2019.. PMLR, 2019.
• Article Гасников А. В. АДАПТИВНЫЙ ПРОКСИМАЛЬНЫЙ МЕТОД ДЛЯ ВАРИАЦИОННЫХ НЕРАВЕНСТВ // Журнал вычислительной математики и математической физики. 2019. Т. 59. № 5. С. 889-894. doi
• Article Гасников А. В., Тюрин А. И. Быстрый градиентный спуск для задач выпуклой минимизации с оракулом, выдающим (δ, L)-модель функции в запрошенной точке // Журнал вычислительной математики и математической физики. 2019. Т. 59. № 7. С. 1137-1150.
• Article Горбунов Э. А., Воронцова Е. А., Гасников А. В. О верхней оценке математического ожидания нормы равномерно распределенного на сфере вектора и явлении концентрации равномерной меры на сфере // Математические заметки. 2019. Т. 106. № 1. С. 13-23. doi
• Article Гасников А. В. Ускоренные безградиентные методы оптимизации с неевклидовым проксимальным оператором // Автоматика и телемеханика. 2019. № 8. С. 149-156. doi (in press)
• 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 Воронцова Е. А., Гасников А. В., Горбунов Э. А. Ускоренный спуск по случайному направлению с неевклидовой прокс-структурой // Автоматика и телемеханика. 2019. Т. 80. № 4. С. 126-143. 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.
• Article Gasnikov A., Гасников Е. В., Nesterov Y. Dual Methods for finding Equilibriums in Mixed Models of Flow Distribution in Large Transportation Networks / Пер. с рус. // Computational Mathematics and Mathematical Physics. 2018. Vol. 58. No. 9. P. 1395-1403. doi
• 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
• Article Гасников А. В., Баяндина А. С., Лагуновская А. А. Безградиентные двухточечные методы решения задач стохастической негладкой выпуклой оптимизации при наличии малых шумов не случайной природы // Автоматика и телемеханика. 2018. № 8. С. 38-49. doi
• Article Гасников А. В., Горбунов Э. А., Ковалёв Д. А., Мохаммед А. А., Черноусова Е. О. Обоснование гипотезы об оптимальных оценках скорости сходимости численных методов выпуклой оптимизации высоких порядков // Компьютерные исследования и моделирование. 2018. Т. 10. № 6. С. 737-753. doi
• Article Anikin A., Gasnikov A., Dvurechensky P., Tyurin Alexander, Chernov A. Dual Approaches to the Minimization of Strongly Convex Functionals with a Simple Structure under Affine Constraints / Пер. с рус. // Computational Mathematics and Mathematical Physics. 2017. Vol. 57. No. 8. P. 1262-1276.
• 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
• Article Гасников А. В., Гасникова Е. В., Нестеров Ю. Е., Чернов А. Об эффективных численных методах решения задач энтропийно-линейного программирования // Журнал вычислительной математики и математической физики. 2016. Т. 56. № 2 (in press)
• Article Гасников А. В., Бабичева Т., Лагуновская А., Двуреченский П. Поиск стохастических равновесий в транспортных моделях равновесного распределения потоков // Журнал вычислительной математики и математической физики. 2016. Т. 56 (in press)
• Article Гасников А. В., Чепурченко К., Мендель М., Гасникова Е. В. Эволюционные выводы энтропийной модели расчета матрицы корреспонденций // Математическое моделирование. 2016. Т. 28 (in press)
• Article Блинкин М. Я., Гасников А. В. Эволюционный вывод простейшей модели бимодального расщепления спроса на городские передвижения // Труды Московского физико-технического института. 2016. Т. 8. № 1 (29). С. 25-31.
• Preprint Anikin A., Dvurechensky P., Gasnikov A., Gornov A., Kamzolov D., Maximov Y., Nesterov Y. Effective Numerical Methods for Huge-Scale Linear Systems with Double-Sparsity and Applications to PageRank / Cornell University. Series arXiv "math". 2015.
• Preprint Anikin A., Dvurechensky P., Gasnikov A., Golov A., Gornov A., Maximov Y., Mendel M., Spokoiny V. Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations / Cornell University. Series arXiv "math". 2015.
• Preprint Gasnikov A., Дорн Ю. В., Dvurechensky P., Maximov Y. Searching equillibriums in Beckmann's and Nesterov--de Palma's models / Cornell University. Series arXiv "math". 2015.
• Article Гасников А. В., Мендель М., Лагуновская А., Бабичева Т. Двухстадийная модель равновесного распределения транспортных потоков // Труды Московского физико-технического института. 2015. Т. 7. № 3
• Article Гасников А. В., Дорн Ю. В., Нестеров Ю. Е., Шпирко С. В. О трехстадийной версии модели стационарной динамики транспортных потоков // Математическое моделирование. 2014. Т. 26. № 6. С. 34-70.
• Article Гасников А. В., Дорн Ю. В., Нурминский Е. А., Шамрай Н. Б. Автомобильные пробки: когда рациональность ведет к коллапсу // Квант. 2013. № 1. С. 13-18.
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  Book Введение в математическое моделирование транспортных потоков / Под общ. ред.: А. В. Гасников. М. : МЦНМО, 2013.
• Book Введение в математическое моделирование транспортных потоков / Под общ. ред.: А. В. Гасников. М. : МФТИ, 2010.