We consider a variable metric linesearch based proximal gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth term. We prove convergence of this iterative algorithm to a critical point if the objective function satisfies the Kurdyka- Lojasiewicz property at each point of its domain, under the assumption that a limit point exists. The proposed method is applied to a wide collection of image processing problems and our numerical tests show that our algorithm results to be flexible, robust and competitive when compared to recently proposed approaches able to address the optimization problems arising in the considered applications.
On the convergence of a linesearch based proximal-gradient method for nonconvex optimization / Bonettini, Silvia; Loris, Ignace; Porta, Federica; Prato, Marco; Rebegoldi, Simone. - In: INVERSE PROBLEMS. - ISSN 0266-5611. - STAMPA. - 33:5(2017), pp. 1-27. [10.1088/1361-6420/aa5bfd]
On the convergence of a linesearch based proximal-gradient method for nonconvex optimization
BONETTINI, Silvia;Porta, Federica;PRATO, Marco;REBEGOLDI, SIMONE
2017
Abstract
We consider a variable metric linesearch based proximal gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth term. We prove convergence of this iterative algorithm to a critical point if the objective function satisfies the Kurdyka- Lojasiewicz property at each point of its domain, under the assumption that a limit point exists. The proposed method is applied to a wide collection of image processing problems and our numerical tests show that our algorithm results to be flexible, robust and competitive when compared to recently proposed approaches able to address the optimization problems arising in the considered applications.File | Dimensione | Formato | |
---|---|---|---|
VOR_On the convergence of a linesearch.pdf
Accesso riservato
Tipologia:
Versione pubblicata dall'editore
Dimensione
3.55 MB
Formato
Adobe PDF
|
3.55 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris