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...
| Hlavní autoři: | , , |
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| Médium: | Journal article |
| Jazyk: | English |
| Vydáno: |
Taylor and Francis
2019
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