Exploiting Higher Order Derivatives in Convex Optimization Methods

Author:

Kamzolov Dmitry,Gasnikov Alexander,Dvurechensky Pavel,Agafonov Artem,Takáč Martin

Publisher

Springer International Publishing

Reference73 articles.

1. Adil D, Bullins B, Jambulapati A, Sachdeva S (2022) Line search-free methods for higher-order smooth monotone variational inequalities. arXiv preprint arXiv:2205.06167

2. Agafonov A, Dvurechensky P, Scutari G, Gasnikov A, Kamzolov D, Lukashevich A, Daneshmand A (2021) An accelerated second-order method for distributed stochastic optimization. In: 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, pp 2407–2413

3. Agafonov A, Kamzolov D, Dvurechensky P, Gasnikov A (2020) Inexact tensor methods and their application to stochastic convex optimization. arXiv preprint arXiv:2012.15636

4. Agafonov A, Kamzolov D, Gasnikov A, Antonakopoulos K, Cevher V, Takáč M (2023) Advancing the lower bounds: an accelerated, stochastic, second-order method with optimal adaptation to inexactness. arXiv preprint arXiv:2309.01570

5. Agarwal N, Hazan E (2018) Lower bounds for higher-order convex optimization. In: Conference On Learning Theory PMLR, pp 774–792

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