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dc.contributor.authorWright, Stephen E., 1962-en_US
dc.date.accessioned2009-10-05T23:58:02Z
dc.date.available2009-10-05T23:58:02Z
dc.date.issued1990en_US
dc.identifier.otherb25836651en_US
dc.identifier.other24331621en_US
dc.identifier.otheren_US
dc.identifier.urihttp://hdl.handle.net/1773/5751
dc.descriptionThesis (Ph. D.)--University of Washington, 1990en_US
dc.description.abstractLarge-scale problems in convex optimization often can be reformulated in primal-dual (minimax) representations having special decomposition properties. Approximation of the resulting high-dimensional problems by restriction to low-dimensional subspaces leads to a family of minimax problems dependent on a parameter. The continuity and convergence properties of this dependence are explored in this dissertation. Examples in optimal control and stochastic programming are considered in which discretizations give rise to large-scale optimization problems. A possible approach to the numerical solution of the discretized problems is described, as well as details of its computer implementation.en_US
dc.format.extentiii, 100 p.en_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.rights.urien_US
dc.subject.otherTheses--Mathematicsen_US
dc.titleConvergence and approximation for primal-dual methods in large-scale optimizationen_US
dc.typeThesisen_US


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