Strict separation of disjoint linear polyhedra ($h^{T}x\le a<b\le h^{T}y$)

Nonempty disjoint linear polyhedra $P=\{x:Ax\le b\}$ and $Q=\{y:Cy\le d\}$ admit $h$ with a strictly positive separation gap. A phase-I linear program minimizing common constraint violation has positive attained optimum. Its Lagrangian dual problem gives nonnegative $\lambda ,\mu$ with $A^T\lambda +C^T\mu =0$ and ${\lambda }^{T}b+{\mu }^{T}d<0$. Then $h=A^T\lambda$ satisfies $h^{T}x\le {\lambda }^{T}b<-{\mu }^{T}d\le h^{T}y$. The polyhedral hypothesis matters; arbitrary disjoint closed convex sets need not have a positive separation gap.