eprintid: 2225
rev_number: 7
eprint_status: archive
userid: 6
dir: disk0/00/00/22/25
datestamp: 2014-07-01 11:02:11
lastmod: 2014-07-01 11:02:11
status_changed: 2014-07-01 11:02:11
type: monograph
metadata_visibility: show
creators_name: Patrinos, Panagiotis
creators_name: Stella, Lorenzo
creators_name: Bemporad, Alberto
creators_id: panagiotis.patrinos@imtlucca.it
creators_id: lorenzo.stella@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
title: Forward-backward truncated Newton methods for convex composite optimization
ispublished: unpub
subjects: QA75
divisions: CSA
full_text_status: none
monograph_type: working_paper
note: A preliminary version of this paper [1] was presented at the 52nd IEEE Conference on Decision and Control, Florence, Italy, December 11, 2013.
abstract: 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. 
date: 2014
date_type: published
number:  
publisher: ArXiv
pages: 39
institution: IMT Institute for Advanced Studies Lucca
official_url: http://arxiv.org/abs/1402.6655
citation:   Patrinos, Panagiotis and Stella, Lorenzo and Bemporad, Alberto  Forward-backward truncated Newton methods for convex composite optimization.  Working Paper  # /2014    ArXiv       (Unpublished)