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arXiv · 2104.02564

Hölder Gradient Descent and Adaptive Regularization Methods in Banach Spaces for First-Order Points

Serge Gratton·Sadok Jerad·Philippe L. Toint
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Abstract

This paper considers optimization of smooth nonconvex functionals in smooth infinite dimensional spaces. A Hölder gradient descent algorithm is first proposed for finding approximate first-order points of regularized polynomial functionals. This method is then applied to analyze the evaluation complexity of an adaptive regularization method which searches for approximate first-order points of functionals with $β$-Hölder continuous derivatives. It is shown that finding an $ε$-approximate first-order point requires at most $O(ε^{-\frac{p+β}{p+β-1}})$ evaluations of the functional and its first $p$ derivatives.

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Serge Gratton, Sadok Jerad, Philippe L. Toint. 2021-04-06. Hölder Gradient Descent and Adaptive Regularization Methods in Banach Spaces for First-Order Points. https://doi.org/10.1080/10556788.2023.2210253

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