Tests for more complicated alternatives should probably be added to it, as a transition to ARMA models. Changes made to AR1 Objective function . Output is labelled with the objective function, not the iteration method. Nonlinear Performance Multiple Optima . For GLS2, the new default is to use a grid search to roughly locate one or more local optima for rho.
Iterations refine each local optimum, to determine the global optimum.
For Exact ML, multiple optima are not a common problem, so no grid search is undertaken by default. Convergence Speed . Quick and reliable iterations are needed to accurately locate local and thus global optima. They have quadratic convergence speed near the optimum. This is different from TSP's usual generalized inverse which sets some change vector elements to zero when the Hessian is indefinite.