Convergence Properties of an Objective-Function-Free Optimization Regularization Algorithm, Including an \(\boldsymbol{\mathcal{O}(\epsilon^{-3/2})}\) Complexity Bound - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue SIAM Journal on Optimization Année : 2023

Convergence Properties of an Objective-Function-Free Optimization Regularization Algorithm, Including an \(\boldsymbol{\mathcal{O}(\epsilon^{-3/2})}\) Complexity Bound

Résumé

An adaptive regularization algorithm for unconstrained nonconvex optimization is presented in which the objective function is never evaluated but only derivatives are used. This algorithm belongs to the class of adaptive regularization methods, for which optimal worst-case complexity results are known for the standard framework where the objective function is evaluated. It is shown in this paper that these excellent complexity bounds are also valid for the new algorithm despite the fact that significantly less information is used.
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Dates et versions

hal-04436388 , version 1 (03-02-2024)

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Citer

Serge Gratton, Sadok Jerad, Philippe L. Toint. Convergence Properties of an Objective-Function-Free Optimization Regularization Algorithm, Including an \(\boldsymbol{\mathcal{O}(\epsilon^{-3/2})}\) Complexity Bound. SIAM Journal on Optimization, 2023, 33 (3), pp.1621-1646. ⟨10.1137/22M1499522⟩. ⟨hal-04436388⟩
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