We study several meshing problems from the
point of view of sampling and numerical optimization.
This specific point of view means optimizing the mesh by
minimizing an objective function that depends on the coordinates at
all the vertices of the mesh. The objective function that we minimize is
similar to the one that defines Centroidal Voronoi Tesselation (quantization noise power).
We propose efficient algorithms to minimize such objective functions and present
some applications to surface and volume meshing (isotropic surface remeshing
and hex-dominant volume meshing).