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On the Convergence of Classical Variational Inequality Algorithms

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dc.creator Magnanti, Thomas L.
dc.creator Perakis, Georgia
dc.date 2004-05-28T19:27:47Z
dc.date 2004-05-28T19:27:47Z
dc.date 1993-05
dc.date.accessioned 2013-10-09T02:38:24Z
dc.date.available 2013-10-09T02:38:24Z
dc.date.issued 2013-10-09
dc.identifier http://hdl.handle.net/1721.1/5201
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description In this paper, we establish global convergence results for projection and relaxation algorithms for solving variational inequality problems, and for the Frank-Wolfe algorithm for solving convex optimization problems defined over general convex sets. The analysis rests upon the condition of f-monotonicity,which we introduced in a previous paper, and which is weaker than the traditional strong monotonicity condition. As part of our development, we provide a new interpretation of a norm condition typically used for establishing convergence of linearization schemes. Applications of our results arize in uncongested as well as congested transportation networks.
dc.format 2044740 bytes
dc.format application/pdf
dc.language en_US
dc.publisher Massachusetts Institute of Technology, Operations Research Center
dc.relation Operations Research Center Working Paper;OR 280-93
dc.title On the Convergence of Classical Variational Inequality Algorithms
dc.type Working Paper


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