Journal of Fourier Analysis and Applications

, Volume 14, Issue 5–6, pp 877–905 | Cite as

Enhancing Sparsity by Reweighted 1 Minimization

  • Emmanuel J. Candès
  • Michael B. WakinEmail author
  • Stephen P. Boyd


It is now well understood that (1) it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements and (2) that this can be done by constrained 1 minimization. In this paper, we study a novel method for sparse signal recovery that in many situations outperforms 1 minimization in the sense that substantially fewer measurements are needed for exact recovery. The algorithm consists of solving a sequence of weighted 1-minimization problems where the weights used for the next iteration are computed from the value of the current solution. We present a series of experiments demonstrating the remarkable performance and broad applicability of this algorithm in the areas of sparse signal recovery, statistical estimation, error correction and image processing. Interestingly, superior gains are also achieved when our method is applied to recover signals with assumed near-sparsity in overcomplete representations—not by reweighting the 1 norm of the coefficient sequence as is common, but by reweighting the 1 norm of the transformed object. An immediate consequence is the possibility of highly efficient data acquisition protocols by improving on a technique known as Compressive Sensing.


1-Minimization Iterative reweighting Underdetermined systems of linear equations Compressive sensing Dantzig selector Sparsity FOCUSS 


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Copyright information

© Birkhäuser Boston 2008

Authors and Affiliations

  • Emmanuel J. Candès
    • 1
  • Michael B. Wakin
    • 2
    Email author
  • Stephen P. Boyd
    • 3
  1. 1.Applied and Computational MathematicsCaltechPasadenaUSA
  2. 2.EngineeringColorado School of MinesGoldenUSA
  3. 3.Department of Electrical EngineeringStanford UniversityStanfordUSA

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