A non-monotone trust-region method with noisy oracles and additional sampling

In this work, we introduce a novel stochastic second-order method, within the framework of a non-monotone trust-region approach, for solving the unconstrained, nonlinear, and non-convex optimization problems arising in the training of deep neural networks. The proposed algorithm makes use of subsamp...

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Veröffentlicht in:	Computational optimization and applications Jg. 89; H. 1; S. 247 - 278
Hauptverfasser:	Krejić, Nataša, Krklec Jerinkić, Nataša, Martínez, Ángeles, Yousefi, Mahsa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.09.2024 Springer Nature B.V
Schlagworte:	Adaptive algorithms Adaptive sampling Algorithms Approximation Artificial neural networks Convergence Convex and Discrete Geometry Convexity Error analysis Image classification Management Science Mathematics Mathematics and Statistics Neural networks Operations Research Operations Research/Decision Theory Optimization Sample size State-of-the-art reviews Statistics 90C53 90C30 65K05 Adaptive sampling Second-order methods Deep neural networks training 90C06 90C90 Stochastic optimization Non-monotone trust-region Quasi-Newton
ISSN:	0926-6003, 1573-2894
Online-Zugang:	Volltext
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