In the book Statistical Learning with Sparsity - The Lasso and Generalizations by Hastie, Tibshirani and Wainwright we can read the following paragraph (p.12):
(...) lasso sets two of the five coefficients to zero, and tends to shrink the coefficients of the others toward zero relative to the full least-squares estimate. In turn, the least-squares fit on the subset of the three predictors tends to expand the lasso estimates away from zero. The nonzero estimates from the lasso tend to be biased toward zero, so the debiasing (...) can often improve the prediction error of the model. This two-stage process is also known as the relaxed lasso (Meinshausen 2007).
They explicitly cite the work by Meinshausen about relaxed lasso however my impression is that the Meinshausen's relaxed lasso is (in short) about performing lasso twice, not lasso+least squares.
Can anyone please clarify that? Is this some sort of shortcut by Hastie & co.?