An interior-point method for solving mathematical programs with
equilibrium constraints (MPECs) is proposed. At each iteration of the
algorithm, a single primal-dual step is computed from each subproblem of
a sequence. Each subproblem is defined as a relaxation of the MPEC with
a nonempty strictly feasible region. In contrast to previous
approaches, the proposed relaxation scheme preserves the nonempty strict
feasibility of each subproblem even in the limit. Local and superlinear
convergence of the algorithm is proved even with a less restrictive
strict complementarity condition than the standard one. Moreover,
mechanisms for inducing global convergence in practice are proposed.
Numerical results on the MacMPEC test problem set demonstrate the
fast-local convergence properties of the algorithm.