Description
The problem of recovering the sparsity pattern of a fixed but unknown vector $\beta^* \in \real^p$ based on a set of $n$ noisy observations arises in a variety of settings, including subset selection in regression, graphical model selection, signal denoising, compressive sensing, and constructive approximation. Of interest are conditions on the model dimension $p$, the sparsity index $s$ (number of non-zero entries in $\beta^*$), and the number of observations $n that are necessary and/or sufficient to ensure asymptotically perfect recovery of the sparsity pattern. This