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The objective is strictly convex, so the minimizer is unique. The Slater condition makes the Karush-Kuhn-Tucker conditions necessary and sufficient. Absorb the box constraints into the Euclidean projection onto a convex set and attach a scalar multiplier $\nu$ to $a^{T}x=b$. Stationarity over the box is equivalent towhile primal feasibility requires $a^T{x}^{\ast }=b$. Coordinatewise, these conditions areThey are also sufficient because they minimize the Lagrangian over the box and satisfy the equality constraint. Thus the projection onto a box-constrained hyperplane reduces to solving the displayed one-dimensional continuous, nonincreasing equation for $\nu$. The multiplier need not be unique on a flat interval, but the projected vector is unique.

Take $a=1$ and $b=k$, sois the capped simplex. A linear objective over this convex polytope attains its maximum at a zero-one extreme point. Choosing the $k$ coordinates at which $x_i$ is largest givesEquivalently, an exchange of weight from a smaller component to a larger one never decreases the objective. Thus the sum of the largest components is the support function ${\sigma }_C$.

Part c now givesBy the projection onto a box-constrained hyperplane, $v=P_C(y/t)$ hasConsequently the proximal operator is evaluated by solving this one-dimensional equation for $\nu$, then substituting the resulting projection.