  The problem of computing the smallest fixed point of an order-preserving map
arises in the study of zero-sum positive stochastic games. It also arises in
static analysis of programs by abstract interpretation. In this context, the
discount rate may be negative. We characterize the minimality of a fixed point
in terms of the nonlinear spectral radius of a certain semidifferential. We
apply this characterization to design a policy iteration algorithm, which
applies to the case of finite state and action spaces. The algorithm returns a
locally minimal fixed point, which turns out to be globally minimal when the
discount rate is nonnegative.
