Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models

The worst-case evaluation complexity for smooth (possibly nonconvex) unconstrained optimization is considered. It is shown that, if one is willing to use derivatives of the objective function up to order p (for p ≥ 1 ) and to assume Lipschitz continuity of the p -th derivative, then an ϵ -approximat...

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