-

 

 

 




Optimization Online





 

A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for ℓ1-regularized least-squares

Margherita Porcelli (margherita.porcelli***at***unibo.it)
Francesco Rinaldi (rinaldi***at***math.unipd.it)

Abstract: The problem of finding sparse solutions to underdetermined systems of linear equations is very common in many fields as e.g. in signal/image processing and statistics. A standard tool for dealing with sparse recovery is the ℓ1-regularized least-squares approach that has recently attracted the attention of many researchers. In this paper, we describe a new version of the two-block nonlinear constrained Gauss- Seidel algorithm for solving ℓ1-regularized least-squares that at each step of the iteration process fixes some variables to zero according to a simple active-set strategy. We prove the global convergence of the new algorithm and we show its efficiency reporting the results of some preliminary numerical experiments.

Keywords: Gauss-Seidel Algorithm, Active Set, Sparse Approximation, ℓ1-regularized least-squares.

Category 1: Nonlinear Optimization

Category 2: Convex and Nonsmooth Optimization

Category 3: Applications -- Science and Engineering

Citation: M. Porcelli, F. Rinaldi, A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for l1-regularized least-squares, Computational Optimization and Applications, 59:3 (2014), pp. 565-589.

Download: [PDF]

Entry Submitted: 06/05/2013
Entry Accepted: 06/05/2013
Entry Last Modified: 11/24/2014

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