A relaxation method based on border basis reduction which improves the
efficiency of Lasserre's approach is proposed to compute the optimum of a
polynomial function on a basic closed semi algebraic set. A new stopping
criterion is given to detect when the relaxation sequence reaches the minimum,
using a sparse flat extension criterion. We also provide a new algorithm to
reconstruct a finite sum of weighted Dirac measures from a truncated sequence
of moments, which can be applied to other sparse reconstruction problems. As an
application, we obtain a new algorithm to compute zero-dimensional minimizer
ideals and the minimizer points or zero-dimensional G-radical ideals.
Experimentations show the impact of this new method on significant benchmarks.Comment: Accepted for publication in Journal of Symbolic Computatio