For a semialgebraic set K in R^n, let P_d(K) be the cone of polynomials in
R^n of degrees at most d that are nonnegative on K. This paper studies the
geometry of its boundary. When K=R^n and d is even, we show that its boundary
lies on the irreducible hypersurface defined by the discriminant of a single
polynomial. When K is a real algebraic variety, we show that P_d(K) lies on the
hypersurface defined by the discriminant of several polynomials. When K is a
general semialgebraic set, we show that P_d(K) lies on a union of hypersurfaces
defined by the discriminantal equations.
We study how to solve sum of squares (SOS) and Lasserre's relaxations for
large scale polynomial optimization. When interior-point type methods are used,
typically only small or moderately large problems could be solved. This paper
proposes the regularization type methods which would solve significantly larger
problems. We first describe these methods for general conic semidefinite
optimization, and then apply them to solve large scale polynomial optimization.
Their efficiency is demonstrated by extensive numerical computations.
We employ positivity of Riesz functionals to establish representing measures
(or approximate representing measures) for truncated multivariate moment
sequences. For a truncated moment sequence $y$, we show that $y$ lies in the
closure of truncated moment sequences admitting representing measures supported
in a prescribed closed set $K \subseteq \re^n$ if and only if the associated
Riesz functional $L_y$ is $K$-positive. For a determining set $K$, we prove
that if $L_y$ is strictly $K$-positive, then $y$ admits a representing measure
supported in $K$.