 Solution

Keep $y\ge 0$ as the domain restriction, introduce nonnegative Lagrange multipliers $s_i$ for $y_i\le 1$, and introduce a free multiplier $t\in \mathbb{R}$ for ${\sum }_i{y}_i=k$. The maximizing Lagrangian isIts supremum over $y\ge 0$ is finite exactly when $s+t1\ge x$, and then equals $kt+{\sum }_i{s}_i$. By linear programming duality, since the primal is feasible and bounded,Minimizing over $s$ for fixed $t$ gives $s_i=(x_i-t{)}_{+}$, the positive part of a real-valued function. Thus the threshold formula for the sum of the largest components isFor a direct attainment check, order the coordinates $x_{[1]}\ge \cdots \ge x_{[n]}$. Any $t\in [x_{[k+1]},x_{[k]}]$ is optimal for $k<n$, while any $t\le x_{[n]}$ is optimal for $k=n$.