(1 + eps)-Approximate Sparse Recovery

@article{Price20111E,
  title={(1 + eps)-Approximate Sparse Recovery},
  author={E. Price and D. Woodruff},
  journal={2011 IEEE 52nd Annual Symposium on Foundations of Computer Science},
  year={2011},
  pages={295-304}
}
  • E. Price, D. Woodruff
  • Published 2011
  • Mathematics, Computer Science
  • 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
  • The problem central to sparse recovery and compressive sensing is that of \emph{stable sparse recovery}: we want a distribution $\math cal{A}$ of matrices $A \in \R^{m \times n}$ such that, for any $x \in \R^n$ and with probability $1 - \delta &gt, 2/3$ over $A \in \math cal{A}$, there is an algorithm to recover $\hat{x}$ from $Ax$ with\begin{align} \norm{p}{\hat{x} - x} \leq C \min_{k\text{-sparse } x'} \norm{p}{x - x'}\end{align}for some constant $C &gt, 1$ and norm $p$. The measurement… CONTINUE READING
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