Generalized Euler squares yield block-orthogonal binary matrices of general row sizes with column size near-maximal and small block coherence, supporting block-sparse recovery.
An asymptotic existence result on compressed sensing matrices
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abstract
For any rational number $h$ and all sufficiently large $n$ we give a deterministic construction for an $n\times \lfloor hn\rfloor$ compressed sensing matrix with $(\ell_1,t)$-recoverability where $t=O(\sqrt{n})$. Our method uses pairwise balanced designs and complex Hadamard matrices in the construction of $\epsilon$-equiangular frames, which we introduce as a generalisation of equiangular tight frames. The method is general and produces good compressed sensing matrices from any appropriately chosen pairwise balanced design. The $(\ell_1,t)$-recoverability performance is specified as a simple function of the parameters of the design. To obtain our asymptotic existence result we prove new results on the existence of pairwise balanced designs in which the numbers of blocks of each size are specified.
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math.CO 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Sparse recovery guarantees for block orthogonal binary matrices constructed via Generalized Euler Squares
Generalized Euler squares yield block-orthogonal binary matrices of general row sizes with column size near-maximal and small block coherence, supporting block-sparse recovery.