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Tensor methods for minimizing convex functions with Hölder continuous higher-order derivatives

Author

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  • NUNES GRAPIGLIA Geovani,

    (Université catholique de Louvain, Belgium)

  • NESTEROV Yurii,

    (Université catholique de Louvain, CORE, Belgium)

Abstract

In this paper we study p-order methods for unconstrained minimization of convex functions that are p-times differentiable (p ≥ 2) with n-Hölder continuous p th derivatives. We propose tensor schemes with and without acceleration. For the schemes without acceleration, we establish iteration complexity bounds of O(e-1/(p+n-1))$ for reducing the functional residual below a given e Î\in (0,1). Assuming that n is known, we obtain an improved complexity bound of O(e-1/(p+n))$ for the corresponding accelerated scheme. For the case in which n is unknown, we present a universal accelerated tensor scheme with iteration complexity of O(e-p/[(p+1)(p+n-1)]). A lower complexity bound of O(e-2/[3(p+n)-2]) is also obtained for this problem class.

Suggested Citation

  • NUNES GRAPIGLIA Geovani, & NESTEROV Yurii,, 2019. "Tensor methods for minimizing convex functions with Hölder continuous higher-order derivatives," LIDAM Discussion Papers CORE 2019028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2019028
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp2019.html
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    Cited by:

    1. Bolte, Jérôme & Glaudin, Lilian & Pauwels, Edouard & Serrurier, Matthieu, 2021. "A Hölderian backtracking method for min-max and min-min problems," TSE Working Papers 21-1243, Toulouse School of Economics (TSE).

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