Convergence rates of RLT and Lasserre-type hierarchies for the generalized moment problem over the simplex and the sphere

Abstract: We consider the generalized moment problem (GMP) over the simplex and the sphere. This is a rich setting and it contains NP-hard problems as special cases, like constructing optimal cubature schemes and rational optimization. Using the Reformulation-Linearization Technique (RLT) and Lasserre-type hierarchies, relaxations of the problem are introduced and analyzed. For our analysis we assume throughout the existence of a dual optimal solution as well as strong duality. For the GMP over the simplex we prove a convergence rate of $O(1/r)$ for a linear programming, RLT-type hierarchy, where $r$ is the level of the hierarchy, using a quantitative version of P\'olya's Positivstellensatz. As an extension of a recent result by Fang and Fawzi [Math. Program., 2020, https://doi.org/10.1007/s10107-020-01537-7] we prove the Lasserre hierarchy of the GMP [Math. Program., Vol. 112, 65-92, 2008] over the sphere has a convergence rate of $O(1/r^2)$. Moreover, we show the introduced linear RLT-relaxation is a generalization of a hierarchy for minimizing forms of degree $d$ over the simplex, introduced by de Klerk, Laurent and Parrilo [J. Theoretical Computer Science, Vol. 361, 210-225, 2006].