A nonlinear conjugate gradient method with complexity guarantees and its application to nonconvex regression (Q6114952)

scientific article; zbMATH DE number 7711263

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English	A nonlinear conjugate gradient method with complexity guarantees and its application to nonconvex regression	scientific article; zbMATH DE number 7711263

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instance of

scholarly article

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title

A nonlinear conjugate gradient method with complexity guarantees and its application to nonconvex regression (English)

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author

Rémi Chan-Renous-Legoubin

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C. W. Royer

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published in

EURO Journal on Computational Optimization

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publication date

12 July 2023

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full work available at URL

https://arxiv.org/abs/2201.08568

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MaRDI profile type

MaRDI publication profile

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cites work

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A globally convergent version of the Polak-Ribière conjugate gradient method

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Algorithm 851

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Benchmarking Derivative-Free Optimization Algorithms

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Cubic regularization of Newton method and its global performance

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Conjugate gradient algorithms in nonconvex optimization

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A Newton-CG algorithm with complexity guarantees for smooth unconstrained optimization

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Complexity Analysis of Second-Order Line-Search Algorithms for Smooth Nonconvex Optimization

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Nonlinear stepsize control, trust regions and regularizations for unconstrained optimization

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Identifiers

zbMATH Open document ID

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10.1016/j.ejco.2022.100044

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6114952