There are two main classes of decoding algorithms for "compressed sensing," those which run time time polynomial in the signal length and those which use sublinear resou...
Compressive sensing aims to recover a sparse or compressible signal from a small set of projections onto random vectors; conventional solutions involve linear programming or greed...
Marco F. Duarte, Michael B. Wakin, Richard G. Bara...
The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, ...
Compressive sensing accurately reconstructs a signal that is sparse in some basis from measurements, generally consisting of the signal’s inner products with Gaussian random vec...
In this paper, we study the number of measurements required to recover a sparse signal in M with L nonzero coefficients from compressed samples in the presence of noise. We conside...