  Rare event simulation and estimation for systems in equilibrium are among the
most challenging topics in molecular dynamics. As was shown by Jarzynski and
others, nonequilibrium forcing can theoretically be used to obtain equilibrium
rare event statistics. The advantage seems to be that the external force can
speed up the sampling of the rare events by biasing the equilibrium
distribution towards a distribution under which the rare events is no longer
rare. Yet algorithmic methods based on Jarzynski's and related results often
fail to be efficient because they are based on sampling in path space. We
present a new method that replaces the path sampling problem by minimization of
a cross-entropy-like functional which boils down to finding the optimal
nonequilibrium forcing. We show how to solve the related optimization problem
in an efficient way by using an iterative strategy based on milestoning.
