Superfast Second-Order Methods for Unconstrained Convex Optimization

In this paper, we present new second-order methods with convergence rate O k - 4 , where k is the iteration counter. This is faster than the existing lower bound for this type of schemes (Agarwal and Hazan in Proceedings of the 31st conference on learning theory, PMLR, pp. 774–792, 2018; Arjevani an...

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Vydané v:	Journal of optimization theory and applications Ročník 191; číslo 1; s. 1 - 30
Hlavný autor:	Nesterov, Yurii
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.10.2021 Springer Nature B.V Springer Verlag
Predmet:	Accuracy Applications of Mathematics Calculus of Variations and Optimal Control; Optimization Computational geometry Convergence Convex analysis Convexity Engineering Iterative methods Learning theory Lower bounds Mathematics Mathematics and Statistics Methods Neighborhoods Operations Research/Decision Theory Optimization Optimization and Control Theory of Computation Convex optimization 90C25 Lower complexity bounds Second-order methods Tensor methods
ISSN:	0022-3239, 1573-2878
On-line prístup:	Získať plný text
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