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Auteurs principaux: Tsipinakis, Nick, Parpas, Panos
Format: Preprint
Publié: 2025
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Accès en ligne:https://arxiv.org/abs/2507.23558
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author Tsipinakis, Nick
Parpas, Panos
author_facet Tsipinakis, Nick
Parpas, Panos
contents Newton's method has been thoroughly studied for the class of self-concordant functions. However, a local analysis specific to strongly self-concordant functions (a subclass of the former) is missing from the literature. The local quadratic rate of strongly self-concordant functions follows, of course, from the known results for self-concordant functions. However, it is not known whether strongly self-concordant functions enjoy better theoretical properties. In this paper, we study the local convergence of Newton's method for this subclass. We show that its quadratic convergence rate differs from that of general self-concordant functions. In particular, it is provably faster for a wide range of objective functions and benefits from a larger region of local convergence. Thus, the results of this paper close the gap in the theoretical understanding of Newton's method applied to strongly self-concordant functions.
format Preprint
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publishDate 2025
record_format arxiv
spellingShingle Convergence rates of Newton's method for strongly self-concordant minimization
Tsipinakis, Nick
Parpas, Panos
Optimization and Control
Newton's method has been thoroughly studied for the class of self-concordant functions. However, a local analysis specific to strongly self-concordant functions (a subclass of the former) is missing from the literature. The local quadratic rate of strongly self-concordant functions follows, of course, from the known results for self-concordant functions. However, it is not known whether strongly self-concordant functions enjoy better theoretical properties. In this paper, we study the local convergence of Newton's method for this subclass. We show that its quadratic convergence rate differs from that of general self-concordant functions. In particular, it is provably faster for a wide range of objective functions and benefits from a larger region of local convergence. Thus, the results of this paper close the gap in the theoretical understanding of Newton's method applied to strongly self-concordant functions.
title Convergence rates of Newton's method for strongly self-concordant minimization
topic Optimization and Control
url https://arxiv.org/abs/2507.23558