In this paper we consider large scale nonlinear least-squares problems for which function and gradient are evaluated with dynamic accuracy and propose a Levenberg-Marquardt method for solving such problems. More precisely, we consider the case in which the exact function to optimize is not available or its evaluation is computationally demanding, but approximations of it are available at any prescribed accuracy level. The proposed method relies on a control of the accuracy level, and imposes an improvement of function approximations when the accuracy is detected to be too low to proceed with the optimization process. We prove global and local convergence and complexity of our procedure and show encouraging numerical results on test problems arising in data assimilation and machine learning.
A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients / stefania bellavia, serge gratton, Elisa Riccietti. - In: NUMERISCHE MATHEMATIK. - ISSN 0029-599X. - STAMPA. - 140:(2018), pp. 791-825. [10.1007/s00211-018-0977-z]
A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients
stefania bellavia;
2018
Abstract
In this paper we consider large scale nonlinear least-squares problems for which function and gradient are evaluated with dynamic accuracy and propose a Levenberg-Marquardt method for solving such problems. More precisely, we consider the case in which the exact function to optimize is not available or its evaluation is computationally demanding, but approximations of it are available at any prescribed accuracy level. The proposed method relies on a control of the accuracy level, and imposes an improvement of function approximations when the accuracy is detected to be too low to proceed with the optimization process. We prove global and local convergence and complexity of our procedure and show encouraging numerical results on test problems arising in data assimilation and machine learning.File | Dimensione | Formato | |
---|---|---|---|
Bellavia2018_Article_ALevenbergMarquardtMethodForLa.pdf
Accesso chiuso
Tipologia:
Pdf editoriale (Version of record)
Licenza:
DRM non definito
Dimensione
818.35 kB
Formato
Adobe PDF
|
818.35 kB | Adobe PDF | Richiedi una copia |
LM_preprint.pdf
accesso aperto
Tipologia:
Versione finale referata (Postprint, Accepted manuscript)
Licenza:
Open Access
Dimensione
589.45 kB
Formato
Adobe PDF
|
589.45 kB | Adobe PDF |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.