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dc.contributor.authorMagnanti, Thomas L.en_US
dc.contributor.authorPerakis, Georgiaen_US
dc.date.accessioned2004-05-28T19:27:59Z
dc.date.available2004-05-28T19:27:59Z
dc.date.issued1996-02en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/5205
dc.description.abstractIn this paper, we propose a concept of polynomiality for variational inequality problems and show how to find a near optimal solution of variational inequality problems in a polynomial number of iterations. To establish this result we build upon insights from several algorithms for linear and nonlinear programs (the ellipsoid algorithm, the method of centers of gravity, the method of inscribed ellipsoids, and Vaidya's algorithm) to develop a unifying geometric framework for solving variational inequality problems. The analysis rests upon the assumption of strong-f-monotonicity, which is weaker than strict and strong monotonicity. Since linear programs satisfy this assumption, the general framework applies to linear programs.en_US
dc.format.extent2566871 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherMassachusetts Institute of Technology, Operations Research Centeren_US
dc.relation.ispartofseriesOperations Research Center Working Paper;OR 276-93en_US
dc.titleA Unifying Geometric Solution Framework and Complexity Analysis for Variational Inequalitiesen_US
dc.typeWorking Paperen_US


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