 Basic inequality for the Lasso

Comparing the Lasso objective at an estimate $\hat{\beta }$ and a target ${\beta }^0$, then expanding the squared loss, bounds the prediction error by a noise-score term and the decrease in the ${\ell }^1$ penalty.