Sparse Approximation, List Decoding, and Uncertainty Principles

Mahmoud Abo Khamis, Anna C. Gilbert, Hung Q. Ngo, Atri Rudra

We consider list versions of sparse approximation problems, where unlike the existing results in sparse approximation that consider situations with unique solutions, we are interested in multiple solutions. We introduce these problems and present the first combinatorial results on the output list size. These generalize and enhance some of the existing results on threshold phenomenon and uncertainty principles in sparse approximations. Our definitions and results are inspired by similar results in list decoding. We also present lower bound examples that bolster our results and show they are of the appropriate size.

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