Porcelli M., Rinaldi F.
Gauss-Seidel Algorithm Set Sparse Approximation l1-regularized leastsquares NUMERICAL ANALYSIS
The problem of finding sparse solutions to underdetermined systems of linear equations is very common in many fields like e.g. signal/image processing and statistics. A standard tool for dealing with sparse recovery is the l1-regularized least-squares approach that has been recently attracting 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 rule. We prove the global convergence of the method and we report numerical results on some test problems showing the efficiency of the implemented algorithm.
Source: ISTI Technical reports, 2013
@techreport{oai:it.cnr:prodotti:272169, title = {A variable fixing version of the two-block nonlinear constrained Gauss-Seidel algorithm for l1-regularized least-squares}, author = {Porcelli M. and Rinaldi F.}, institution = {ISTI Technical reports, 2013}, year = {2013} }