On the asymptotic rate of convergence of Stochastic Newton algorithms and their Weighted Averaged versions

Author:

Boyer Claire,Godichon-Baggioni AntoineORCID

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Control and Optimization

Reference17 articles.

1. Bach, F., Moulines, E.: Non-strongly-convex smooth stochastic approximation with convergence rate o (1/n). Adv. Neural Inf. Process. Syst. pp. 773–781 (2013)

2. Bercu, B., Godichon, A., Portier, B.: An efficient stochastic newton algorithm for parameter estimation in logistic regressions. SIAM J. Control. Optim. 58(1), 348–367 (2020)

3. Bottou, L., Curtis, F.E., Nocedal, J.: Optimization methods for large-scale machine learning. SIAM Rev. 60(2), 223–311 (2018)

4. Byrd, R.H., Hansen, S.L., Nocedal, J., Singer, Y.: A stochastic quasi-newton method for large-scale optimization. SIAM J. Optim. 26(2), 1008–1031 (2016)

5. Castera, C., Bolte, J., Févotte, C., Pauwels, E.: An inertial newton algorithm for deep learning. arXiv preprint arXiv:1905.12278, (2019)

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