Improved bounds for sparse recovery from subsampled random convolutions
Improved bounds for sparse recovery from subsampled random convolutions is a scholarly work, published in 2018 in ''Annals of Applied Probability''. The main subjects of the publication include binary logarithm, Gaussian, positron emission tomography, graph dimension, Restricted isometry property, chaining, discrete mathematics, minification, compressed sensing, combinatorics, mathematics, and algorithm. The authors study the recovery of sparse vectors from subsampled random convolutions via $\ell_1$-minimization.