Superlinear Convergence of an Interior-Point Method for Monotone Variational Inequalities

Daniel Ralph and Stephen Wright

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.

Research Report No. 3, 1996, Department of Mathematics, University of Melbourne; Preprint MCS-P556-0196, Mathematics and Computer Science Division, Argonne National Lab.