  We present an efficient framework for solving constrained global non-convex
polynomial optimization problems. We prove the existence of an equivalent
nonlinear reformulation of such problems that possesses essentially no spurious
local minima. We show through numerical experiments that polynomial scaling in
dimension and degree is achievable for computing the optimal value and location
of previously intractable global constrained polynomial optimization problems
in high dimension.
