Name
Abstract | ||
|---|---|---|
An important observation in compressed sensing is that the l(0) minimizer of an underdetermined linear system is equal to the l(1) minimizer when there exists a sparse solution vector and a certain restricted isometry property holds. Here, we develop a continuous analogue of this observation and show that the best L-0 and L-1 polynomial approximants of a polynomial that is corrupted on a set of small measure are nearly equal. We demonstrate an error localization property of best L-1 polynomial approximants and use our observations to develop an improved algorithm for computing best L-1 polynomial approximants to continuous functions. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1137/19M1242860 | SIAM JOURNAL ON NUMERICAL ANALYSIS |
Keywords | DocType | Volume |
polynomial approximation, best L-1, compressed sensing, best L-0, restricted isometry property, error localization | Journal | 59 |
Issue | ISSN | Citations |
1 | 0036-1429 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuji Nakatsukasa | 1 | 97 | 17.74 |
| Alex Townsend | 2 | 113 | 15.69 |