Convergence and approximation for primal-dual methods in large-scale optimization

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Convergence and approximation for primal-dual methods in large-scale optimization

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dc.contributor.author Wright, Stephen E., 1962- en_US
dc.date.accessioned 2009-10-05T23:58:02Z
dc.date.available 2009-10-05T23:58:02Z
dc.date.issued 1990 en_US
dc.identifier.other b25836651 en_US
dc.identifier.other 24331621 en_US
dc.identifier.other en_US
dc.identifier.uri http://hdl.handle.net/1773/5751
dc.description Thesis (Ph. D.)--University of Washington, 1990 en_US
dc.description.abstract Large-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.extent iii, 100 p. en_US
dc.language.iso en_US en_US
dc.rights.uri en_US
dc.subject.other Theses--Mathematics en_US
dc.title Convergence and approximation for primal-dual methods in large-scale optimization en_US
dc.type Thesis en_US


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