  The uniform sampling of convex regions in high dimension is an important
computational issue, from both theoretical and applied point of view. The
hit-and-run montecarlo algorithms are the most efficient methods known to
perform it and one of their bottlenecks relies in the difficulty of escaping
from tight corners in high dimension. Inspired by optimized montecarlo methods
used in statistical mechanics we define a new algorithm by overelaxing the
hit-and-run dynamics. We made numerical simulations on high dimensional
simplices in order to test its performances, pointing out its improved ability
to escape from angles and finally apply it to an inference problem in the
steady state dynamics of metabolic networks.
