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We first exhibit that under similar RIP conditions with that in the standard l q case, the mixed l 2/l q recovery method can assuredly to recover any block-sparse signal, irrespective to the locations of non-zero blocks.
Additionally, RIP conditions must be calibrated to minimize reassociation of RBPs with mRNA in vitro after cell lysis, which has been observed under some conditions [ 74] but not others [ 75].
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Most of the existing CS methods guarantee an exact or approximate reconstruction if the measurement matrix which is determined by the measurement strategy is well-conditioned (e.g. satisfies RIP condition [3]).
It guarantees a good reconstruction if Φ satisfies RIP condition.
This conjecture says that if the RIP condition is given by δK+ 1< ϵ then ϵ should be strictly smaller than 1 / K.
When Φ satisfies the restricted isometry property (RIP) condition [18], the BP approach is an effective reconstruction approach and does not require the exactness of the sparse signal.
The advantages of the greedy algorithm-based approaches are fast, stable, uniform guarantees, however it requires a slightly stronger condition on the restricted isometry property (RIP) condition than first category[23].
The correlation between two different columns of the sampling matrix is weak, i.e., Φ k T Φ k ≫ Φ k T Φ k ′ (k ′ ≠ k), which satisfies the basic RIP condition for compressive sampling[6].
In this study, we assume the sensing matrix X satisfies the RIP condition, i.e., δ k + 1 < 1 k + 1, which guarantees the perfect recovery without noise perturbation [15].
Since we assumed that δ k + 1 < 1 k + 1, the sensing matrix X meets the RIP condition with δ k < 1 k − 1 + 1 ≤ 1, i.e., δ k <1.
In [22], theorem 3.3 shows that for any t > 1 and any K, S > 2, a random subset Q of average cardinality M=left( CtS log Kright) log left( CtS log Kright){ log}^2 S (21)satisfies the RIP condition with probability of at least 1 − 5e − ct and that the C with possible indices denotes absolute constants.
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