I am specifically thinking about the reconstruction error of L1-minimization from compressed measurements with noise. I know a bound from (8) in The restricted isometry property and its implications for compressed sensing, but I was wondering if there is anything newer and or tighter that I am not aware of. Additionally, I would like to relate $\delta_{2s}$ to the size of e.g. a Gaussian measurement matrix. It seems I can do that from Lemma 3.1 and (3.22) in Decoding by linear programming, but how tight is (3.22) and can I get closer?
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According to Michael McCoy on LinkedIn:
The best bound for Gaussian matrices is in: W. Xu and B. Hassibi, Precise Stability Phase Transitions for l1 Minimization: A Unified Geometric Framework, IEEE Trans. Inf. Theory. Oct. 2011
See, for example, the curves in Fig. 1.
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1$\begingroup$ Would please give out your understanding of the paper, in non-technical way, preferably? $\endgroup$– MimSaadApr 11, 2017 at 9:30