  The Lasserre hierarchy of semidefinite programming (SDP) relaxations is an
effective scheme for finding computationally feasible SDP approximations of
polynomial optimization over compact semi-algebraic sets. In this paper, we
show that, for convex polynomial optimization, the Lasserre hierarchy with a
slightly extended quadratic module always converges asymptotically even in the
face of non-compact semi-algebraic feasible sets. We do this by exploiting a
coercivity property of convex polynomials that are bounded below. We further
establish that the positive definiteness of the Hessian of the associated
Lagrangian at a saddle-point (rather than the objective function at each
minimizer) guarantees finite convergence of the hierarchy. We obtain finite
convergence by first establishing a new sum-of-squares polynomial
representation of convex polynomials over convex semi-algebraic sets under a
saddle-point condition. We finally prove that the existence of a saddle-point
of the Lagrangian for a convex polynomial program is also necessary for the
hierarchy to have finite convergence.
