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

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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.

Contact: wright@mcs.anl.gov