= Lasso dropout knot
{c}
{title2=$\gamma_{\rm drop}=\min_{j\in A:\,-\beta_j/d_j>0}(-\beta_j/d_j)$}
On a least-angle segment $\beta(\gamma)=\beta+\gamma d$, a nonzero active <regression coefficient> reaches zero at the displayed first positive root. Continue only as far as the first dropout, inactive-entry event or zero-correlation endpoint. At a dropout remove the vanishing <regression coefficient> from the active direction and recompute. This preserves agreement between nonzero <regression coefficient> signs and residual-score signs, which the <Karush-Kuhn-Tucker conditions for the Lasso> require. A removed <regression coefficient> may re-enter later; the active set need not grow monotonically.
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