An Improved Lower Bound for Sparse Reconstruction from Subsampled Hadamard Matrices

03/28/2019

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by   Jarosław Błasiok, et al.

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We give a short argument that yields a new lower bound on the number of subsampled rows from a bounded, orthonormal matrix necessary to form a matrix with the restricted isometry property. We show that a matrix formed by uniformly subsampling rows of an N × N Hadamard matrix contains a K-sparse vector in the kernel, unless the number of subsampled rows is Ω(K K (N/K)) --- our lower bound applies whenever (K, N/K) > ^C N. Containing a sparse vector in the kernel precludes not only the restricted isometry property, but more generally the application of those matrices for uniform sparse recovery.