A convex form of degree larger than one is always nonnegative since it vanishes together with its gradient at the origin. In 2007, Parrilo asked if convex forms are always sums of squares. A few years later, Blekherman answered the question in the negative by showing through volume arguments that for high enough number of … Read more
The Lasserre hierarchy of semidefinite programming approximations to convex polynomial optimization problems is known to converge finitely under some assumptions. [J.B. Lasserre. Convexity in semialgebraic geometry and polynomial optimization. SIAM J. Optim. 19, 1995-2014, 2009.] We give a new proof of the finite convergence property, that does not require the assumption that the Hessian of … Read more
We consider the problem of minimizing a polynomial on the hypercube [0,1]^n and derive new error bounds for the hierarchy of semidefinite programming approximations to this problem corresponding to the Positivstellensatz of Schmuedgen (1991). The main tool we employ is Bernstein approximations of polynomials, which also gives constructive proofs and degree bounds for positivity certificates … Read more
Lov\’ asz and Schrijver [1991] have constructed semidefinite relaxations for the stable set polytope of a graph $G=(V,E)$ by a sequence of lift-and-project operations; their procedure finds the stable set polytope in at most $\alpha(G)$ steps, where $\alpha(G)$ is the stability number of $G$. Two other hierarchies of semidefinite bounds for the stability number have … Read more
We consider the problem of computing the minimum value $p_{\min}$ taken by a polynomial $p(x)$ of degree $d$ over the standard simplex $\Delta$. This is an NP-hard problem already for degree $d=2$. For any integer $k\ge 1$, by minimizing $p(x)$ over the set of rational points in $\Delta$ with denominator $k$, one obtains a hierarchy … Read more