Minimizing Uniformly Convex Functions by Cubic Regularization of Newton Method

Nikita Doikov () and Yurii Nesterov ()
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Nikita Doikov: Université catholique de Louvain, ICTEAM
Yurii Nesterov: Université catholique de Louvain, LIDAM/CORE, Belgium

No 3235, LIDAM Reprints CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: In this paper, we study the iteration complexity of cubic regularization of Newton method for solving composite minimization problems with uniformly convex objective. We introduce the notion of second-order condition number of a certain degree and justify the linear rate of convergence in a nondegenerate case for the method with an adaptive estimate of the regularization parameter. The algorithm automatically achieves the best possible global complexity bound among different problem classes of uniformly convex objective functions with Hölder continuous Hessian of the smooth part of the objective. As a byproduct of our developments, we justify an intuitively plausible result that the global iteration complexity of the Newton method is always better than that of the gradient method on the class of strongly convex functions with uniformly bounded second derivative.

Keywords: Newton method; Cubic regularization; Global complexity bounds; Strong convexity; Uniform convexity (search for similar items in EconPapers)
Pages: 23
Date: 2023-01-01
Note: In: Journal of Optimization Theory and Applications, 2021, vol. 189(1), p. 317-339
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvrp:3235

DOI: 10.1007/s10957-021-01838-7

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