  This note studies a method for the efficient estimation of a finite number of
unknown parameters from linear equations, which are perturbed by Gaussian
noise.
  In case the unknown parameters have only few nonzero entries, the proposed
estimator performs more efficiently than a traditional approach.
  The method consists of three steps:
  (1) a classical Least Squares Estimate (LSE),
  (2) the support is recovered through a Linear Programming (LP) optimization
problem which can be computed using a soft-thresholding step,
  (3) a de-biasing step using a LSE on the estimated support set.
  The main contribution of this note is a formal derivation of an associated
ORACLE property of the final estimate.
  That is, when the number of samples is large enough, the estimate is shown to
equal the LSE based on the support of the {\em true} parameters.
