interior-point methods are some of the most effective and widely used methods to finding local minimizers of large-scale non-convex optimization problems. in this talk, we introduce three different mechanisms for ensuring global convergence to second-order local minimizers from arbitrary feasible starting points by solving a sequence of trust-region subproblems defined by quadratic models of a shifted primal-dual penalty-barrier merit function. each of these methods begins by solving the trust-region subproblem to form a new trial point, and proceeds to refine the trial iterate until a sufficient-decrease condition is met. we suggest two different definitions of the trust region, and provide numerical results comparing each of the different approaches.