  We study the problem of maximizing information divergence from a new
perspective using logarithmic Voronoi polytopes. We show that for linear
models, the maximum is always achieved at the boundary of the probability
simplex. For toric models, we present an algorithm that combines the
combinatorics of the chamber complex with numerical algebraic geometry. We pay
special attention to reducible models and models of maximum likelihood degree
one.
