  We prove existence and uniqueness of the minimizer for the average geodesic
distance to the points of a geodesically convex set on the sphere. This implies
a corresponding existence and uniqueness result for an optimal algorithm for
halfspace learning, when data and target functions are drawn from the uniform
distribution.
