INFORMAZIONI SU QUESTO ARTICOLO

Cita

1. S. Bellavia, G. Gurioli, and B. Morini, Theoretical study of an adaptive cubic regularization method with dynamic inexact hessian information, arXiv:1808.06239, 2018.Search in Google Scholar

2. S. Bellavia, N. Krejić, and N. Krklec Jerinkić, Subsampled inexact newton methods for minimizing large sums of convex functions, IMA J. Numerical Analysis, 2019.10.1093/imanum/drz027Search in Google Scholar

3. A. Berahas, R. Bollapragada, and J. Nocedal, An investigation of newton-sketch and subsampled newton methods, arXiv:1705.06211v3, 2018.Search in Google Scholar

4. E. Birgin, N. Krejić, and J. Martínez, On the employment of inexact restoration for the minimization of functions whose evaluation is subject to programming errors, Mathematics of Computation, vol. 87, pp. 1307–1326, 2018.Search in Google Scholar

5. D. Blatt, A. O. Hero, and H. Gauchman, A convergent incremental gradient method with a constant step size, SIAM Journal of Optimization, vol. 18, no. 1, pp. 29–51, 2007.10.1137/040615961Search in Google Scholar

6. R. Bollapragada, R. R. Byrd, and J. Nocedal, Exact and inexact subsampled newton methods for optimization, IMA Journal Numerical Analysis, 2018.10.1093/imanum/dry009Search in Google Scholar

7. L. Bottou, F. E. Curtis, and J. Nocedal, Optimization methods for large-scale machine learning, SIAM Review, vol. 60, no. 2, pp. 223–311, 2018.10.1137/16M1080173Search in Google Scholar

8. L. Bottou, Stochastic gradient learning in neural networks, Proceedings of Neuro-Nimes, vol. 91, no. 8, p. 12, 1991.Search in Google Scholar

9. R. Byrd, G. Chin, J. Nocedal, and Y. Wu, Sample size selection in optimization methods for machine learning, Mathematical Programming, vol. 134, no. 1, pp. 127–155, 2012.10.1007/s10107-012-0572-5Search in Google Scholar

10. M. P. Friedlander and M. Schmidt, Hybrid deterministic-stochastic methods for data fitting, SIAM Journal on Scientific Computing, vol. 34, no. 3, pp. 1380–1405, 2012.Search in Google Scholar

11. R. Johnson and T. Zhang, Accelerating stochastic gradient descent using predictive variance reduction, Advances in Neural Information Processing Systems, 2013.Search in Google Scholar

12. N. Krejić and N. Krklec Jerinkić, Nonmonotone line search methods with variable sample size, Numerical Algorithms, vol. 68, no. 4, pp. 711–739, 2015.10.1007/s11075-014-9869-1Search in Google Scholar

13. F. Roosta-Khorasani and M. Mahoney, Sub-sampled newton methods, Mathematical Programming, vol. 174, pp. 293–326, 2019.10.1007/s10107-018-1346-5Search in Google Scholar

14. N. N. Schraudolph, J. Yu, and S. Günter, A stochastic quasi-newton method for online convex optimization, SAIS International Conference on Artificial Intelligence and Statistics, pp. 436–443, 2007.Search in Google Scholar

15. C. Tan, S. Ma, Y. H. Dai, and Y. Qian, Barzilai-borwein step size for stochastic gradient descent, Advances in Neural Information Processing Systems, vol. 29, pp. 685–693, 2016.Search in Google Scholar

16. Z. Yang, C. Wang, Z. Zhang, and J. Li, Random barzilai-borwein step size for mini-batch algorithms, Engineering Applications of Artificial Intelligence, vol. 72, pp. 124–135, 2018.10.1016/j.engappai.2018.03.017Search in Google Scholar

17. J. Barzilai and J. M. Borwein, Two-point step size gradient method, IMA J. Numerical Analysis, vol. 8, no. 1, pp. 141–148, 1988.10.1093/imanum/8.1.141Search in Google Scholar

18. E. G. Birgin, J. M. Martínez, and M. Raydan, Spectral projected gradient methods: Review and perspectives, Journal of Statistical Software, vol. 60, 2014.10.18637/jss.v060.i03Search in Google Scholar

19. Y. H. Dai, W. W. Hager, K. Schittkowski, and H. Zhang, The cyclic barzilai-borwein method for unconstrained optimization, IMA Journal Numerical Analysis, vol. 26, no. 3, pp. 604–627, 2006.10.1093/imanum/drl006Search in Google Scholar

20. R. Fletcher, On the barzilai-borwein gradient method, in Optimization and Control with Applications, Applied Optimization (L. Qi, K. Teo, and X. Yang, eds.), vol. 96, pp. 235–256, Springer, 2005.10.1007/0-387-24255-4_10Search in Google Scholar

21. D. di Serafino, V. Ruggiero, G. Toraldo, and L. Zanni, On the steplength selection in gradient methods for unconstrained optimization, Applied Mathematics and Computation, vol. 318, pp. 176–195, 2006.10.1016/j.amc.2017.07.037Search in Google Scholar

22. M. Raydan, The barzilai and borwein gradient method for the large scale unconstrained minimization problem, SIAM Journal Optimization, vol. 7, no. 1, pp. 26–33, 1997.10.1137/S1052623494266365Search in Google Scholar

23. N. Krejić and N. Krklec Jerinkić, Spectral projected gradient method for stochastic optimization, Journal of Global Optimization, vol. 73, pp. 59–81, 2018.10.1007/s10898-018-0682-6Search in Google Scholar

24. D. H. Li and M. Fukushima, A derivative-free line search and global convergence of broyden-like method for nonlinear equations, Optimization Methods and Software, vol. 13, no. 3, pp. 181–201, 2000.10.1080/10556780008805782Search in Google Scholar

25. G. N. Grapiglia and E. Sachs, On the worst-case evaluation complexity of nonmonotone line search algorithms, Computational Optimization and applications, vol. 68, no. 3, pp. 555–577, 2017.10.1007/s10589-017-9928-3Search in Google Scholar

26. Causality workbench team, a marketing dataset. http://www.causality.inf.ethz.ch/data/CINA.html, 2008.Search in Google Scholar

27. M. Lichman, Uci machine learning repository. https://archive.ics.uci.edu/ml/index.php, 2013.Search in Google Scholar

eISSN:
2038-0909
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Mathematics, Numerical and Computational Mathematics, Applied Mathematics