SPIRAL: a superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization-Reference-Cited by-同舟云学术

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SPIRAL: a superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization

Published:2024-03-29 Issue:1 Volume:88 Page:71-106
ISSN:0926-6003
Container-title:Computational Optimization and Applications
language:en
Short-container-title:Comput Optim Appl

Author:

Behmandpoor Pourya^ORCID,Latafat Puya,Themelis Andreas,Moonen Marc,Patrinos Panagiotis

Funder

Fonds De La Recherche Scientifique - FNRS

FWO

Japan Society for the Promotion of Science London

H2020 Marie Sklodowska-Curie Actions

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10589-023-00550-8.pdf

Reference74 articles.

1. Ahookhosh, M., Themelis, A., Patrinos, P.: A Bregman forward-backward linesearch algorithm for nonconvex composite optimization: superlinear convergence to nonisolated local minima. SIAM J. Optim. 31(1), 653–685 (2021). https://doi.org/10.1137/19M1264783

2. Aragón Artacho, F.J., Belyakov, A., Dontchev, A.L., López, M.: Local convergence of quasi-Newton methods under metric regularity. Comput. Optim. Appl. 58(1), 225–247 (2014)

3. Bauschke, H.H., Bolte, J., Teboulle, M.: A descent lemma beyond Lipschitz gradient continuity: first-order methods revisited and applications. Math. Oper. Res. 42(2), 330–348 (2017)

4. Beck, A., Teboulle, M.: Mirror descent and nonlinear projected subgradient methods for convex optimization. Oper. Res. Lett. 31(3), 167–175 (2003)

5. Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. Neural Networks: Tricks of the Trade: Second Edition, pp. 437–478 (2012)

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1. An interior proximal gradient method for nonconvex optimization;Open Journal of Mathematical Optimization;2024-07-09

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