= Solution
The term $\gamma\|\beta\|_2^2/2$ is strictly convex, while the remaining terms are convex, so the <elastic net> objective is strictly convex and its minimizer is unique. If two columns of $X$ are identical, swapping their coefficients leaves the objective unchanged. Uniqueness then forces those coefficients to be equal.
The <Karush-Kuhn-Tucker conditions> are
$$
\frac1nX^T(X\widehat\beta-Y)+\gamma\widehat\beta
+\lambda\widehat z=0,
$$
where
$$
\widehat z_j=
\begin{cases}
\operatorname{sgn}(\widehat\beta_j),&\widehat\beta_j\ne0,\\
[-1,1],&\widehat\beta_j=0.
\end{cases}
$$
Assume $Y=X\beta^0$ and $\operatorname{sgn}(\widehat\beta)=\operatorname{sgn}(\beta^0)=s$. The active equations give
$$
(X_S^TX_S+n\gamma I)\widehat\beta_S
=X_S^TX_S\beta_S^0-n\lambda s_S.
$$
The inactive KKT inequalities become
$$
\boxed{\left\|X_N^TX_S(X_S^TX_S+n\gamma I)^{-1}
\left(\frac\gamma\lambda\beta_S^0+s_S\right)
\right\|_\infty\leq1.}
$$
Conversely, define $\widetilde\beta_N=0$ and
$$
\widetilde\beta_S=(X_S^TX_S+n\gamma I)^{-1}
(X_S^TX_S\beta_S^0-n\lambda s_S).
$$
If the displayed inequality holds and $\operatorname{sgn}(\widetilde\beta_S)=s_S$, the active equations and inactive inequalities together satisfy every KKT condition. Convexity and uniqueness imply $\widetilde\beta=\widehat\beta$, proving sign recovery.
The final sign condition printed in the question omits the factor $n$ before $\lambda$. For the objective as stated, the corrected expression above is required; without that correction, the claimed converse does not follow from the KKT equations.
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