All issues

List of references:

1. Anaconda python дистрибутив, официальный веб-сайт. — Электронный ресурс. — https://www.anaconda.com. — дата обращения: 20.02.2019.
   • Anaconda python distribution official website. — Electronic resource. — https://www.anaconda.com. — accessed 20.02.2019.
2. А. С. Баяндина, А. В. Гасников, А. А. Лагуновская. Безградиентные двухточечные методы решения задач стохастической негладкой выпуклой оптимизации при наличии малых шумов неслучайной природы // Автоматика и телемеханика. — 2018. — № 8. — С. 38–49.
   • A. S. Bayandina, A. V. Gasnikov, A. A. Lagunovskaya. Gradient-free two-point methods for solving stochastic nonsmooth convex optimization problems with small non-random noises // Automation and Remote Control. — 2018. — V. 79, no. 8. — P. 1399–1408. — DOI: 10.1134/S0005117918080039. — MathSciNet: MR3860298.
   • A. S. Bayandina, A. V. Gasnikov, A. A. Lagunovskaya. Bezgradientnye dvukhtochechnye metody resheniya zadach stokhasticheskoi negladkoi vypukloi optimizatsii pri nalichii malykh shumov ne sluchainoi prirody // Automation and Remote Control. — 2018. — no. 8. — P. 38–49. — in Russian. — Math-Net: Mi eng/at14643.
3. Ф. П. Васильев. Методы оптимизации. — М: МЦНМО, 2011. — Т. 2. — 433 с.
   • F. P. Vasiliev. Methods of Optimization. — Moscow: MCCME, 2011. — V. 2. — 433 p. — in Russian.
4. Е. А. Воронцова, А. В. Гасников, Э. А. Горбунов. Ускоренные спуски по случайному направлению с неевклидовой прокс-структурой // Автоматика и телемеханика. — 2019. — № 4. — С. 126–143.
   • E. A. Vorontsova, A. V. Gasnikov, E. A. Gorbunov. Accelerated Directional Search with non-Euclidean prox-structure // Automation and Remote Control. — 2019. — no. 4. — P. 126–143. — in Russian. — Math-Net: Mi eng/at15270.
5. А. В. Гасников. Современные численные методы оптимизации. Метод универсального градиентного спуска. — М: МФТИ, 2018. — 240 с.
   • A. V. Gasnikov. Modern numerical optimization methods. universal gradient descent method. — Moscow: MIPT, 2018. — 240 p. — in Russian.
6. А. С. Немировский, Д. Б. Юдин. Сложность задач и эффективность методов оптимизации. — М: Наука, 1979.
   • A. S. Nemirovski, D. B. Yudin. Problem Complexity and Method Efficiency in Optimization. — New York: Wiley-Interscience, 1983. — MathSciNet: MR0702836.
   • A. S. Nemirovski, D. B. Yudin. Slozhnost’ zadach i effektivnost’ metodov optimizatsii. — Moscow: Nauka, 1979. — Russ. ed. — in Russian.
7. Б. Т. Поляк. Введение в оптимизацию. — М: Наука, 1983.
   • B. T. Polyak. Introduction to Optimization. — New York: Optimization Software, 1987. — MathSciNet: MR1099605.
   • B. T. Polyak. Vvedenie v optimizatsiyu. — Moscow: Nauka, 1983. — Russ. ed. — in Russian. — MathSciNet: MR0719196.
8. Н. З. Шор. Методы минимизации недифференцируемых функций и их приложения. — Киев: Наукова Думка, 1979.
   • N. Z. Shor. Minimization Methods for Non-Differentiable Functions. — Series in Computational Mathematics. — Springer, 1985. — MathSciNet: MR0775136.
   • N. Z. Shor. Metody minimizatsii nedifferentsiruemykh funktsii i ikh prilozheniya. — Kiev: Naukova Dumka, 1979. — Russ. ed. — in Russian.
9. A. Bayandina, P. Dvurechensky, A. Gasnikov, F. Stonyakin, A. Titov. Mirror descent and convex optimization problems with non-smooth inequality constraints / Large-Scale and Distributed Optimization. — Springer International Publishing, 2018. — P. 181–215. — P. Giselsson, A. Rantzer (eds.). — Chap. 8. — MathSciNet: MR3888675.
10. L. Bogolubsky, P. Dvurechensky, A. Gasnikov, G. Gusev, Yu. Nesterov, A. M. Raigorodskii, A. Tikhonov, M. Zhukovskii. Learning supervised pagerank with gradient-based and gradient-free optimization methods / Advances in Neural Information Processing Systems. — Curran Associates, Inc, 2016. — V. 29. — P. 4914–4922. — D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett (eds.).
11. S. Bubec. Convex optimization: algorithms and complexity // Foundations and Trends in Machine Learning. — 2015. — V. 8, no. 3–4. — P. 231–357. — DOI: 10.1561/2200000050.
12. Y. Chen, G. Lan, Y. Ouyang. Optimal primal-dual methods for a class of saddle point problems // SIAM Journal on Optimization. — 2014. — V. 24, no. 4. — P. 1779–1814. — DOI: 10.1137/130919362. — MathSciNet: MR3272627.
13. C. D. Dang, G. Lan. Stochastic Block Mirror Descent Methods for Nonsmooth and Stochastic Optimization // SIAM J. on Optimization. — 2015. — V. 25, no. 2. — P. 856–881. — DOI: 10.1137/130936361. — MathSciNet: MR3341135.
14. O. Devolder, F. Glineur, Yu. Nesterov. First-order methods of smooth convex optimization with inexact oracle // Mathematical Programming. — 2014. — V. 146, no. 1–2. — P. 37–75. — DOI: 10.1007/s10107-013-0677-5. — MathSciNet: MR3232608.
15. P. Dvurechensky, A. Gasnikov. Stochastic Intermediate Gradient Method for Convex Problems with Stochastic Inexact Oracle // Journal of Optimization Theory and Applications. — 2016. — V. 171, no. 1. — P. 121–145. — DOI: 10.1007/s10957-016-0999-6. — MathSciNet: MR3547846.
16. A. V. Gasnikov, A. A. Lagunovskaya, I. N. Usmanova, F. A. Fedorenko. Gradient-free proximal methods with inexact oracle for convex stochastic nonsmooth optimization problems on the simplex // Automation and Remote Control. — 2016. — V. 77, no. 11. — P. 2018–2034. — DOI: 10.1134/S0005117916110114. — MathSciNet: MR3664202.
17. Y.-T. Lee, A. Sidford, S. C.-W. Wong. A faster cutting plane method and its implications for combinatorial and convex optimization. — 2015. — E-print. — https://arxiv.org/pdf/1508.04874.pdf. — accessed 02.01.2019. — MathSciNet: MR3473356.
18. A. Nemirovski. Lectures on modern convex optimization analysis, algorithms, and engineering applications. — Philadelphia: SIAM, 2015. — http://www2.isye.gatech.edu/~nemirovs/Lect_ModConvOpt.pdf. — accessed 02.01.2019.
19. Yu. Nesterov. Primal-dual subgradient methods for convex problems // Mathematical Programming. — 2009. — V. 120, no. 1. — P. 221–259. — DOI: 10.1007/s10107-007-0149-x. — MathSciNet: MR2496434.
20. Yu. Nesterov, S. Shpirko. Primal-Dual Subgradient Method for Huge-Scale Linear Conic Problems // SIAM Journal on Optimization. — 2014. — V. 24, no. 3. — P. 1444–1457. — DOI: 10.1137/130929345. — MathSciNet: MR3257645.
21. Yu. Nesterov. Lectures on convex optimization. — Springer, 2018. — MathSciNet: MR3839649.
22. R. Tappenden, P. Richt´arik, Gondzio, J. . Inexact Coordinate Descent: Complexity and Preconditioning // Journal of Optimization Theory and Applications. — 2016. — V. 170, no. 1. — P. 144–176. — DOI: 10.1007/s10957-016-0867-4. — MathSciNet: MR3513271.