IDEAS home Printed from https://ideas.repec.org/r/cor/louvrp/2965.html
   My bibliography  Save this item

Relatively smooth convex optimization by first-order methods, and applications

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Nesterov, Yurii, 2022. "Quartic Regularity," LIDAM Discussion Papers CORE 2022001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Vincenzo Bonifaci, 2021. "A Laplacian approach to $$\ell _1$$ ℓ 1 -norm minimization," Computational Optimization and Applications, Springer, vol. 79(2), pages 441-469, June.
  3. Xue Gao & Xingju Cai & Xiangfeng Wang & Deren Han, 2023. "An alternating structure-adapted Bregman proximal gradient descent algorithm for constrained nonconvex nonsmooth optimization problems and its inertial variant," Journal of Global Optimization, Springer, vol. 87(1), pages 277-300, September.
  4. Zhongming Wu & Chongshou Li & Min Li & Andrew Lim, 2021. "Inertial proximal gradient methods with Bregman regularization for a class of nonconvex optimization problems," Journal of Global Optimization, Springer, vol. 79(3), pages 617-644, March.
  5. Shota Takahashi & Akiko Takeda, 2025. "Approximate bregman proximal gradient algorithm for relatively smooth nonconvex optimization," Computational Optimization and Applications, Springer, vol. 90(1), pages 227-256, January.
  6. Yurii Nesterov, 2021. "Superfast Second-Order Methods for Unconstrained Convex Optimization," Journal of Optimization Theory and Applications, Springer, vol. 191(1), pages 1-30, October.
  7. Emanuel Laude & Peter Ochs & Daniel Cremers, 2020. "Bregman Proximal Mappings and Bregman–Moreau Envelopes Under Relative Prox-Regularity," Journal of Optimization Theory and Applications, Springer, vol. 184(3), pages 724-761, March.
  8. Leandro Farias Maia & David Huckleberry Gutman & Ryan Christopher Hughes, 2024. "The Inexact Cyclic Block Proximal Gradient Method and Properties of Inexact Proximal Maps," Journal of Optimization Theory and Applications, Springer, vol. 201(2), pages 668-698, May.
  9. Flavia Chorobura & Ion Necoara, 2024. "Coordinate descent methods beyond smoothness and separability," Computational Optimization and Applications, Springer, vol. 88(1), pages 107-149, May.
  10. Masoud Ahookhosh & Le Thi Khanh Hien & Nicolas Gillis & Panagiotis Patrinos, 2021. "Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization," Computational Optimization and Applications, Springer, vol. 79(3), pages 681-715, July.
  11. Zehui Liu & Qingsong Wang & Chunfeng Cui & Yong Xia, 2025. "Inertial accelerated stochastic mirror descent for large-scale generalized tensor CP decomposition," Computational Optimization and Applications, Springer, vol. 91(1), pages 201-233, May.
  12. Doikov, Nikita & Nesterov, Yurii, 2020. "Affine-invariant contracting-point methods for Convex Optimization," LIDAM Discussion Papers CORE 2020029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  13. Pourya Behmandpoor & Puya Latafat & Andreas Themelis & Marc Moonen & Panagiotis Patrinos, 2024. "SPIRAL: a superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization," Computational Optimization and Applications, Springer, vol. 88(1), pages 71-106, May.
  14. Zamani, Moslem & Abbaszadehpeivasti, Hadi & de Klerk, Etienne, 2024. "The exact worst-case convergence rate of the alternating direction method of multipliers," Other publications TiSEM f30ae9e6-ed19-423f-bd1e-0, Tilburg University, School of Economics and Management.
  15. Abbaszadehpeivasti, Hadi, 2024. "Performance analysis of optimization methods for machine learning," Other publications TiSEM 3050a62d-1a1f-494e-99ef-7, Tilburg University, School of Economics and Management.
  16. Xin Jiang & Lieven Vandenberghe, 2023. "Bregman Three-Operator Splitting Methods," Journal of Optimization Theory and Applications, Springer, vol. 196(3), pages 936-972, March.
  17. Ziyuan Wang & Andreas Themelis & Hongjia Ou & Xianfu Wang, 2024. "A Mirror Inertial Forward–Reflected–Backward Splitting: Convergence Analysis Beyond Convexity and Lipschitz Smoothness," Journal of Optimization Theory and Applications, Springer, vol. 203(2), pages 1127-1159, November.
  18. Hui Zhang & Yu-Hong Dai & Lei Guo & Wei Peng, 2021. "Proximal-Like Incremental Aggregated Gradient Method with Linear Convergence Under Bregman Distance Growth Conditions," Mathematics of Operations Research, INFORMS, vol. 46(1), pages 61-81, February.
  19. Radu-Alexandru Dragomir & Alexandre d’Aspremont & Jérôme Bolte, 2021. "Quartic First-Order Methods for Low-Rank Minimization," Journal of Optimization Theory and Applications, Springer, vol. 189(2), pages 341-363, May.
  20. Yin Liu & Sam Davanloo Tajbakhsh, 2023. "Stochastic Composition Optimization of Functions Without Lipschitz Continuous Gradient," Journal of Optimization Theory and Applications, Springer, vol. 198(1), pages 239-289, July.
  21. Alkousa, Mohammad & Stonyakin, Fedor & Gasnikov, Alexander & Abdo, Asmaa & Alcheikh, Mohammad, 2024. "Higher degree inexact model for optimization problems," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
  22. Alberto De Marchi & Andreas Themelis, 2022. "Proximal Gradient Algorithms Under Local Lipschitz Gradient Continuity," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 771-794, September.
  23. Filip Hanzely & Peter Richtárik & Lin Xiao, 2021. "Accelerated Bregman proximal gradient methods for relatively smooth convex optimization," Computational Optimization and Applications, Springer, vol. 79(2), pages 405-440, June.
  24. Mahesh Chandra Mukkamala & Jalal Fadili & Peter Ochs, 2022. "Global convergence of model function based Bregman proximal minimization algorithms," Journal of Global Optimization, Springer, vol. 83(4), pages 753-781, August.
  25. Masoud Ahookhosh, 2019. "Accelerated first-order methods for large-scale convex optimization: nearly optimal complexity under strong convexity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(3), pages 319-353, June.
  26. Masoud Ahookhosh & Le Thi Khanh Hien & Nicolas Gillis & Panagiotis Patrinos, 2021. "A Block Inertial Bregman Proximal Algorithm for Nonsmooth Nonconvex Problems with Application to Symmetric Nonnegative Matrix Tri-Factorization," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 234-258, July.
  27. Chung-En Tsai & Hao-Chung Cheng & Yen-Huan Li, 2022. "Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States," Papers 2210.00997, arXiv.org, revised Sep 2023.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.