## $$\ell^1$$-analysis minimization and generalized (co-)sparsity: when does recovery succeed?(English)Zbl 1460.94021

Summary: This paper investigates the problem of stable signal estimation from undersampled, noisy sub-Gaussian measurements under the assumption of a cosparse model. Based on generalized notions of sparsity, a novel recovery guarantee for the $$\ell^1$$-analysis basis pursuit is derived, enabling accurate predictions of its sample complexity. The bounds on the number of required measurements explicitly depend on the Gram matrix of the analysis operator and therefore account for its mutual coherence structure. The presented results defy conventional wisdom which promotes the sparsity of analysis coefficients as the crucial quantity to be studied. In fact, this paradigm breaks down in many situations of interest, for instance, when applying a redundant (multilevel) frame as analysis prior. In contrast, the proposed sampling-rate bounds reliably capture the recovery capability of various practical examples. The proofs are based on establishing a sophisticated upper bound on the conic Gaussian mean width associated with the underlying $$\ell^1$$-analysis polytope.

### MSC:

 94A12 Signal theory (characterization, reconstruction, filtering, etc.) 65K05 Numerical mathematical programming methods 90C25 Convex programming 90C90 Applications of mathematical programming
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### References:

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