Complete the square to write the objective as
Let and , so while feasibility requires . The reverse triangle inequality gives
The objective is therefore at least zero. Equality is attained by , which lies on the boundary. Equality requires the vectors to have the same direction and , so this minimizer is unique. Hence
It also satisfies the Karush-Kuhn-Tucker conditions with . The direct distance argument proves global optimality even though the exterior of a disk is not a convex set; this is nearest-point minimization outside a Euclidean ball.

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