  We study various constraints and conditions on the true coefficient vector
and on the design matrix to establish non-asymptotic oracle inequalities for
the prediction error, estimation accuracy and variable selection for the Lasso
estimator in high dimensional sparse regression models. We review results from
the literature and we provide simpler and detailed derivation for several
boundedness theorems. In addition, we complement the theory with illustrated
examples.
