On the Performance of Sparse Recovery Via lp-Minimization (0 <= p <= 1)

  title={On the Performance of Sparse Recovery Via lp-Minimization (0 <= p <= 1)},
  author={Meng Wang and Weiyu Xu and Ao Tang},
  journal={IEEE Trans. Inf. Theory},
It is known that a high-dimensional sparse vector x* in TV can be recovered from low-dimensional measurements y = Ax* where Am×n(m <; n) is the measurement matrix. In this paper, with A being a random Gaussian matrix, we investigate the recovering ability of lp-minimization (0 ≤ p ≤ 1) as p varies, where lp-minimization returns a vector with the least lp quasi norm among all the vectors x satisfying Ax = y. Besides analyzing the performance of strong recovery where lp-minimization is re quired… 
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