-

 

 

 




Optimization Online





 

A Local MM Subspace Method for Solving Constrained Variational Problems in Image Recovery

Emilie Chouzenoux(emilie.chouzenoux***at***centralesupelec.fr)
Ségolène Martin(segolene.martin***at***centralesupelec.fr)
Jean-Christophe Pesquet(jean-christophe.pesquet***at***centralesupelec.fr)

Abstract: This article introduces a new Penalized Majorization-Minimization Subspace algorithm (P-MMS) for solving smooth, constrained optimization problems. In short, our approach consists of embedding a subspace algorithm in an inexact exterior penalty procedure. The subspace strategy, combined with a Majoration-Minimization step-size search, takes great advantage of the smoothness of the penalized cost function, while the penalty method allows to handle a wide range of constraints. The main drawback of exterior penalty approaches, namely ill-conditioning for large values of the penalty parameter, is overcome by using a trust-region like technique. The convergence of the resulting algorithm is analyzed. Numerical experiments carried out on two large-scale image recovery applications demonstrate that, compared with state-of-the-art algorithms, the proposed method performs well in terms of computational time.

Keywords: constrained optimization ; smooth optimization ; subspace acceleration ; exterior penalty method ; Majorization-Minimization ; wavelet restoration ; PET reconstruction

Category 1: Nonlinear Optimization (Constrained Nonlinear Optimization )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )

Category 3: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation:

Download: [PDF]

Entry Submitted: 09/22/2021
Entry Accepted: 09/22/2021
Entry Last Modified: 09/22/2021

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society