  The analysis of polycrystalline materials benefits greatly from accurate
quantitative descriptions of their grain structures. Laguerre tessellations
approximate such grain structures very well. However, it is a quite challenging
problem to fit a Laguerre tessellation to tomographic data, as a
high-dimensional optimization problem with many local minima must be solved. In
this paper, we formulate a version of this optimization problem that can be
solved quickly using the cross-entropy method, a robust stochastic optimization
technique that can avoid becoming trapped in local minima. We demonstrate the
effectiveness of our approach by applying it to both artificially generated and
experimentally produced tomographic data.
