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Restricted Isometry Constants Where \ell ^{p} Sparse Recovery Can Fail for 0\ll p \leq 1

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2 Author(s)
Michael Evan Davies ; Joint Res. Inst. for Signal & Image Process., Edinburgh Univ., Edinburgh ; RÉmi Gribonval

This paper investigates conditions under which the solution of an underdetermined linear system with minimal lscrp norm, 0 < p les 1, is guaranteed to be also the sparsest one. Matrices are constructed with restricted isometry constants (RIC) delta2m arbitrarily close to 1/radic2 ap 0.707 where sparse recovery with p = 1 fails for at least one m-sparse vector, as well as matrices with delta2m arbitrarily close to one where lscr1 minimization succeeds for any m-sparse vector. This highlights the pessimism of sparse recovery prediction based on the RIC, and indicates that there is limited room for improving over the best known positive results of Foucart and Lai, which guarantee that lscr1 minimization recovers all m-sparse vectors for any matrix with delta2m < 2(3 - radic2)/7 ap 0.4531. These constructions are a by-product of tight conditions for lscrp recovery (0 les p les 1) with matrices of unit spectral norm, which are expressed in terms of the minimal singular values of 2m-column submatrices. Compared to lscr1 minimization, lscrp minimization recovery failure is shown to be only slightly delayed in terms of the RIC values. Furthermore in this case the minimization is nonconvex and it is important to consider the specific minimization algorithm being used. It is shown that when lscrp optimization is attempted using an iterative reweighted lscr1 scheme, failure can still occur for delta2m arbitrarily close to 1/radic2.

Published in:

IEEE Transactions on Information Theory  (Volume:55 ,  Issue: 5 )