@incollection{eprints2226,
           month = {December},
       publisher = {IEEE},
           pages = {2358--2363},
          author = {Panagiotis Patrinos and Alberto Bemporad},
       booktitle = {52nd IEEE Conference on Decision and Control},
           title = {Proximal Newton methods for convex composite optimization},
            year = {2013},
        keywords = {Approximation algorithms, Approximation methods, Convergence, Gradient methods, Radio frequency, Signal processing algorithms},
        abstract = {This paper proposes two proximal Newton methods for convex nonsmooth optimization problems in composite form. The algorithms are based on a new continuously differentiable exact penalty function, namely the Composite Moreau Envelope. 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 solution of a linear system of usually small dimension.},
             url = {http://eprints.imtlucca.it/2226/}
}