Iteratively Re-weighted Least Squares minimization: Proof of faster than linear rate for sparse recovery

@article{Daubechies2008IterativelyRL,
  title={Iteratively Re-weighted Least Squares minimization: Proof of faster than linear rate for sparse recovery},
  author={Ingrid Daubechies and Ronald A. DeVore and Massimo Fornasier and C. Sinan G{\"u}nt{\"u}rk},
  journal={2008 42nd Annual Conference on Information Sciences and Systems},
  year={2008},
  pages={26-29}
}
Given an mtimesN matrix Phi, with m<N, the system of equations Phix=y is typically underdetermined and has infinitely many solutions. Various forms of optimization can extract a "best" solution. One of the oldest is to select the one with minimal lscr2 norm. It has been shown that in many applications a better choice is the minimal lscr1 norm solution. This is the case in compressive sensing, when sparse solutions are sought. The minimal lscr1 norm solution can be found by using linear… CONTINUE READING
22 Citations
27 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 22 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 27 references

Finite Algorithms in Optimization and Data Analysis

  • M. R. Osborne
  • John Wiley & Sons Ltd., Chichester
  • 1985
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…