A concise second-order complexity analysis for unconstrained optimization using high-order regularized models

An adaptive regularization algorithm is proposed that uses Taylor models of the objective of order p, p≥2, of the unconstrained objective function, and that is guaranteed to find a first- and second-order critical point in at most O(max{ϵ−p+1p1,ϵ−p+1p−12}) function and derivatives evaluations, where...

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Detalhes bibliográficos
Principais autores: Cartis, C, Gould, N, Toint, P
Formato: Journal article
Idioma:English
Publicado em: Taylor and Francis 2019