On the convergence of a linesearch based proximal-gradient method for nonconvex optimization

We consider a variable metric line-search 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 minimum 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 if compared to recently proposed approaches able to address the optimization problems arising in the considered applications.