# Proximal Newton methods for convex composite optimization

Patrinos, Panagiotis and Bemporad, Alberto Proximal Newton methods for convex composite optimization. In: 52nd IEEE Conference on Decision and Control. IEEE, pp. 2358-2363. ISBN 978-1-4673-5714-2 (2013)

Full text not available from this repository.

## 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.

Item Type: Book Section 10.1109/CDC.2013.6760233 Approximation algorithms, Approximation methods, Convergence, Gradient methods, Radio frequency, Signal processing algorithms Q Science > QA Mathematics > QA75 Electronic computers. Computer science Computer Science and Applications Ms T. Iannizzi 01 Jul 2014 11:13 01 Jul 2014 11:13 http://eprints.imtlucca.it/id/eprint/2226