The proximal-proximal gradient algorithm

TitleThe proximal-proximal gradient algorithm
Publication TypeSINBAD Presentation
AuthorsTing Kei Pong, Michael P. Friedlander, Felix J. Herrmann
PublisherSINBAD
Year of Publication2013
Abstract

In many applications, one has to minimize the sum of a smooth loss function modeling misfit and a regularization term inducing structures. In this talk, we consider the case when the regularization is a composition of a convex function, whose proximal mapping is easy to compute, and a nonzero linear map. Such instances arise in system identification and realization problems. In this talk, we present a new algorithm, the proximal-proximal gradient algorithm, which admits easy subproblems. Our algorithm reduces to the proximal gradient algorithm if the linear map is just the identity map, and can be viewed as "very inexact" inexact proximal gradient algorithm. We show that the whole sequence generated from the algorithm converges to an optimal solution, and establish an upper bound on iteration complexity.

KeywordsPresentation, private, SINBAD, SINBADFALL2013, SLIM
URLhttps://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2013/Fall/pong2013SINBADppg/pong2013SINBADppg.pdf
Citation Keypong2013SINBADppg