The problem of finding sparse solutions to underdetermined systems of linear equations arises in several applications (e.g., signal and image processing, compressive sensing, statistical inference). A standard tool for dealing with sparse recovery is the $\ell_1$-regularized least squares approach that has been recently attracting the attention of many researchers. In this paper, we describe an active set estimate (i.e., an estimate of the indices of the zero variables in the optimal solution) for the considered problem that tries to quickly identify as many active variables as possible at a given point, while guaranteeing that some approximate optimality conditions are satisfied. A relevant feature of the estimate is that it gives a significant reduction of the objective function when setting to zero all those variables estimated to be active. This enables us to easily embed it into a given globally converging algorithmic framework. In particular, we include our estimate into a block coordinate descent algorithm for $\ell_1$-regularized least squares, analyze the convergence properties of this new active set method, and prove that its basic version converges with a linear rate. Finally, we report some numerical results showing the effectiveness of the approach.


  1. $\ell_1$-regularized least squares
  2. active set
  3. sparse optimization

MSC codes

  1. 65K05
  2. 90C25
  3. 90C06

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Information & Authors


Published In

cover image SIAM Journal on Optimization
SIAM Journal on Optimization
Pages: 781 - 809
ISSN (online): 1095-7189


Submitted: 19 December 2014
Accepted: 21 December 2015
Published online: 23 March 2016


  1. $\ell_1$-regularized least squares
  2. active set
  3. sparse optimization

MSC codes

  1. 65K05
  2. 90C25
  3. 90C06



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