On the Performance of Sparse Recovery Via $\ell _p$ -Minimization $(0 \leq p \leq 1)$

It is known that a high-dimensional sparse vector x* in TV can be recovered from low-dimensional measurements y = Ax* where A^m×n(m <; n) is the measurement matrix. In this paper, with A being a random Gaussian matrix, we investigate the recovering ability of ℓ_p-minimization (0 ≤ p ≤ 1) as p varies, where ℓ_p-minimization returns a vector with the least ℓ_p quasi norm among all the vectors x satisfying Ax = y. Besides analyzing the performance of strong recovery where ℓ_p-minimization is re quired to recover all the sparse vectors up to certain sparsity, we also for the first time analyze the performance of "weak" recovery of ℓ_p -minimization (0 ≤ p <; 1) where the aim is to recover all the sparse vectors on one support with a fixed sign pattern. When α(:= ^m/_n) → 1, we provide sharp thresholds of the sparsity ratio (i.e., percentage of nonzero entries of a vector) that differentiates the success and failure via ℓ_p -minimization. For strong recovery, the threshold strictly decreases from 0.5 to 0.239 as p increases from 0 to 1. Surprisingly, for weak recovery, the threshold is 2/3 for all p in [0, 1), while the threshold is 1 for ℓ₁-minimization. We also explicitly demonstrate that ℓ_p-minimization (p <; 1) can re turn a denser solution than ℓ_p-minimization. For any a G (0.1), we provide bounds of the sparsity ratio for strong recovery and weak recovery, respectively, below which ℓ_p-minimization succeeds. Our bound of strong recovery improves on the existing bounds when a is large. In particular, regarding the recovery threshold, this paper argues that ℓ_p-minimization has a higher threshold with smaller p for strong recovery; the threshold is the same for all p for sectional - - recovery; and ℓ_p -minimization can outperform ℓ_p-minimization for weak recovery. These are in contrast to traditional wisdom that ℓ_p-minimization, though computationally more expensive, always has better sparse recovery ability than ℓ₀ -minimization since it is closer to ℓ₁-minimization. Finally, we provide an intuitive explanation to our findings. Numerical examples are also used to un ambiguously confirm and illustrate the theoretical predictions.