Approximating Hessians in unconstrained optimization arising from discretized problems

V. Malmedy, Philippe Toint

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Résumé

We consider Hessian approximation schemes for large-scale unconstrained optimization in the context of discretized problems. The considered Hessians typically present a nontrivial sparsity and partial separability structure. This allows iterative quasi-Newton methods to solve them despite of their size. Structured finite-difference methods and updating schemes based on the secant equation are presented and compared numerically inside the multilevel trust-region algorithm proposed by Gratton et al. (IMA J. Numer. Anal. 28(4):827-861, 2008).
langue originaleAnglais
Pages (de - à)1-22
Nombre de pages22
journalComputational Optimization and Applications
Volume50
Numéro de publication1
Les DOIs
Etat de la publicationPublié - 1 sept. 2011

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