## Abstract

Numerical experiments have indicated that the reweighted $\ell_1$-minimization performs exceptionally well in locating sparse solutions of underdetermined linear systems of equations. We show that reweighted $\ell_1$-methods are intrinsically associated with the minimization of the so-called merit functions for sparsity, which are essentially concave approximations to the cardinality function. Based on this observation, we further show that a family of reweighted $\ell_1$-algorithms can be systematically derived from the perspective of concave optimization through the linearization technique. In order to conduct a unified convergence analysis for this family of algorithms, we introduce the concept of the range space property (RSP) of a matrix and prove that if its adjoint has this property, the reweighted $\ell_1$-algorithm can find a sparse solution to the underdetermined linear system provided that the merit function for sparsity is properly chosen. In particular, some convergence conditions for the Candès--Wakin--Boyd method and the recent $\ell_p$-quasi-norm-based reweighted $\ell_1$-method can be obtained as special cases of the general framework.

Read More: http://epubs.siam.org/doi/abs/10.1137/110847445

Read More: http://epubs.siam.org/doi/abs/10.1137/110847445

Original language | English |
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Article number | 1 |

Pages (from-to) | 1065–1088 |

Number of pages | 24 |

Journal | SIAM Journal on Optimization |

Volume | 22 |

Issue number | 3 |

DOIs | |

Publication status | Published - 2012 |

## Keywords

- Reweighted ℓ1-minimization, sparse solution, underdetermined linear system, concave minimization, merit function for sparsity, compressed sensing