Name
Abstract | ||
|---|---|---|
In this paper, we study the orthogonal least squares (OLS) algorithm for sparse recovery. On the one hand, we show that if the sampling matrix $mathbf{A}$ satisfies the restricted isometry property (RIP) of order $K + 1$ with isometry constant $$ delta_{K + 1} u003c frac{1}{sqrt{K+1}}, $$ then OLS exactly recovers the support of any $K$-sparse vector $mathbf{x}$ from its samples $mathbf{y} = mathbf{A} mathbf{x}$ in $K$ iterations. On the other hand, we show that OLS may not be able to recover the support of a $K$-sparse vector $mathbf{x}$ in $K$ iterations for some $K$ if $$ delta_{K + 1} geq frac{1}{sqrt{K+frac{1}{4}}}. $$ |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Information Theory | Discrete mathematics,Orthogonal least squares,Mathematical optimization,Matrix (mathematics),Isometry,Mathematics,Restricted isometry property |
DocType | Volume | Citations |
Journal | abs/1611.07628 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jinming wen | 1 | 103 | 14.52 |
| Jian Wang | 2 | 216 | 13.26 |
| Qinyu Zhang | 3 | 0 | 2.03 |