Aliens Needs A Curve

The Aliens trick removes an exact-kk constraint by charging a penalty λ\lambda per selected item and binary searching λ\lambda. What has to be true, and what is the failure mode when it is not?

A

The relaxed problem must be solvable in O(n)O(n). Varying λ\lambda sweeps a line of that slope across the lower hull of the feasible (g(x),f(x))(g(x), f(x)) points, and every feasible point is the minimizer for some λ\lambda, so any penalty eventually produces it.

B

The optimum as a function of the count must be convex. Varying λ\lambda sweeps a line of that slope across the lower hull of the feasible (g(x),f(x))(g(x), f(x)) points, and a point strictly inside the hull is never the minimizer for any λ\lambda, so no penalty ever produces it.

C

The optimum as a function of the count must be monotone. Varying λ\lambda sweeps a line of that slope across the lower hull of the feasible (g(x),f(x))(g(x), f(x)) points, and a point strictly inside the hull is still reachable for some λ\lambda, so the reported objective stays optimal.

D

The count kk must be a power of two. Varying λ\lambda sweeps a line of that slope across the lower hull of the feasible (g(x),f(x))(g(x), f(x)) points, and a point whose count is not a power of two is never the minimizer for any λ\lambda, so no penalty ever produces it.