Subsampled cubic regularization method for finite-sum minimization
Author:
Affiliation:
1. IME, Universidade Federal de Goias, Goiânia, Brazil
Funder
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Publisher
Informa UK Limited
Link
https://www.tandfonline.com/doi/pdf/10.1080/02331934.2024.2318258
Reference26 articles.
1. Stochastic analysis of an adaptive cubic regularization method under inexact gradient evaluations and dynamic Hessian accuracy
2. Adaptive cubic regularization methods with dynamic inexact Hessian information and applications to finite-sum minimization
3. Bellavia S Gurioli G Morini B et al. Quadratic and cubic regularisation methods with inexact function and random derivatives for finite-sum minimisation. In: Proceedings of the 21st International Conference on Computational Science and Its Applications (ICCSA); Vol. 2021; 2021. p. 258–267.
4. Subsampled inexact Newton methods for minimizing large sums of convex functions
5. Inexact restoration with subsampled trust-region methods for finite-sum minimization
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