%X This paper proposes two proximal Newton-CG methods for convex nonsmooth optimization problems in composite form. The algorithms are based on a a reformulation of the original nonsmooth problem as the unconstrained minimization of a continuously differentiable function, namely the forward-backward envelope (FBE). The first algorithm is based on a standard line search strategy, whereas the second one combines the global efficiency estimates of the corresponding first-order methods, while achieving fast asymptotic convergence rates. Furthermore, they are computationally attractive since each Newton iteration requires the approximate solution of a linear system of usually small dimension. 
%D 2014
%L eprints2225
%A Panagiotis Patrinos
%A Lorenzo Stella
%A Alberto Bemporad
%N  
%T Forward-backward truncated Newton methods for convex composite optimization
%I ArXiv
%O A preliminary version of this paper [1] was presented at the 52nd IEEE Conference on Decision and Control, Florence, Italy, December 11, 2013.