Superlinear convergence of an interior-point method for monotone variational inequalities

We describe an infeasible-interior-point algorithm for monotone variational inequality problems and prove that it converges globally and superlinearly under standard conditions plus a constant rank constraint qualification. The latter condition represents a generalization of the two types of assumptions made in existing superlinear analyses; namely, linearity of the constraints and linear independence of the active constraint gradients