On the convergence of a linesearch based proximal-gradient method for nonconvex optimization. (3rd March 2017)

• Record Type: Journal Article
• Authors: Bonettini, S; Loris, I; Porta, F; Prato, M; Rebegoldi, S
• Is Part Of: Inverse problems. Volume 33:Number 5(2017:May)
• Publication Date: 2017-03-03
• Subjects: proximal gradient methods -- variable metric -- line search methods -- image processing applications; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/aa5bfd
• Languages: English
• ISSNs: 0266-5611

Abstract

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–Łojasiewicz 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.