Неравенство Лежанского – Поляка – Лоясевича и сходимость метода проекции градиента
- Авторы: Балашов М.В.1
-
Учреждения:
- Институт проблем управления РАН им. В. А. Трапезникова
- Выпуск: Том 23, № 1 (2023)
- Страницы: 4-10
- Раздел: Статьи
- URL: https://bakhtiniada.ru/1816-9791/article/view/250828
- DOI: https://doi.org/10.18500/1816-9791-2023-23-1-4-10
- EDN: https://elibrary.ru/ZSKZLA
- ID: 250828
Цитировать
Полный текст
Аннотация
Об авторах
Максим Викторович Балашов
Институт проблем управления РАН им. В. А. ТрапезниковаМосква, ул. Профсоюзная, 65
Список литературы
- Lee J. M. Manifolds and differential geometry. Graduate Studies in Mathematics. Rhode Island, AMS Providence, 2009. Vol. 107. 671 p. https://doi.org/10.1090/gsm/107
- Lojasiewicz S. Sur le probleme de la division. Studia Mathematica, 1959, vol. 18, pp. 87–136. https://doi.org/10.4064/sm-18-1-87-136
- Balashov M. V., Polyak B. T., Tremba A. A. Gradient projection and conditional gradient methods for constrained nonconvex minimization. Numerical Functional Analysis and Optimization, 2020, vol. 41, iss. 7, pp. 822–849. https://doi.org/10.1080/01630563.2019.1704780
- Karimi H., Nutini J., Schmidt M. Linear convergence of gradient and proximal-gradient methods under the Polyak – Lojasiewicz condition. In: Frasconi P., Landwehr N., Manco G., Vreeken J. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Lecture Notes in Computer Science, vol. 9851. Cham, Springer, 2016, pp. 795–811. https://doi.org/10.1007/978-3-319-46128-1_50
- Schneider R., Uschmajew A. Convergence results for projected line-search methods on varieties of low-rank matricies via Lojasiewicz inequality. SIAM Journal on Optimization, 2015, vol. 25, iss. 1, pp. 622–646. https://doi.org/10.1137/140957822
- Merlet B., Nguyen T. N. Convergence to equilibrium for discretizations of gradient-like flows on Riemannian manifolds. Differential Integral Equations, 2013, vol. 26, iss. 5/6, pp. 571–602. https://doi.org/10.57262/die/1363266079
- Vial J.-Ph. Strong and weak convexity of sets and functions. Mathematics of Operations Research, 1983, vol. 8, iss. 2, pp. 231–259. https://doi.org/10.1287/moor.8.2.231
- Balashov M. V. The gradient projection algorithm for smooth sets and functions in nonconvex case. Set-Valued and Variational Analysis, 2021, vol. 29, pp. 341–360. https://doi.org/10.1007/s11228-020-00550-4
- Balashov M. V. Stability of minimization problems and the error bound condition. Set-Valued and Variational Analysis, 2022, vol. 30, pp. 1061–1076. https://doi.org/10.1007/s11228-022-00634-3
- Ivanov G. E. Slabo vypuklye mnozhestva i funktsii: teoriya i prilozhenie [Weakly Convex Sets and Functions: Theory and Application]. Moscow, Fizmatlit, 2006. 351 p. (in Russian).
- Balashov M. V., Kamalov R. A. The gradient projection method with Armijo’s step size on manifolds. Computational Mathematics and Mathematical Physics, 2021, vol. 61, iss. 1, pp. 1776–1786. https://doi.org/10.1134/S0965542521110038
- Adly S., Nacry F., Thibault L. Preservation of prox-regularity of sets with applications to constrained optimization. SIAM Journal on Optimization, 2016, vol. 26, iss. 1, pp. 448–473. https://doi.org/10.1137/15M1032739
- Polyak B. T. Introduction to Optimization. New York, Optimization Software, 1987. 464 p.
- Balashov M. V., Tremba A. A. Error bound conditions and convergence of optimization methods on smooth and proximally smooth manifolds. Optimization: A Journal of Mathematical Programming and Operations Research, 2020, vol. 71, iss. 3, pp. 711–735. https://doi.org/10.1080/02331934.2020.1812066
Дополнительные файлы
