Stochastic Perturbation Theory

dc.contributor.authorStewart, G. W.en_US
dc.date.accessioned2004-05-31T22:20:40Z
dc.date.available2004-05-31T22:20:40Z
dc.date.created1988-10en_US
dc.date.issued1998-10-15en_US
dc.description.abstractAppeared in SIAM Review 32 (1990) 576--610. In this paper classical matrix perturbation theory is approached from a probabilistic point of view. The perturbed quantity is approximated by a first order perturbation expansion, in which the perturbation is assumed to be random. This permits the computation of statistics estimating the variation in the perturbed quantity. Up to the higher order terms that are ignored in the expansion, these statistics tend to be more realistic than perturbation bounds obtained in terms of norms. The technique is applied to a number of problems in matrix perturbation theory, including least squares and the eigenvalue problem. Additional files are available via anonymous ftp at: thales.cs.umd.edu in the directory /ftp/pub/reports (Also cross-referenced as UMIACS-TR-88-76)en_US
dc.format.extent296182 bytes
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/1903/540
dc.language.isoen_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-2129en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-88-76en_US
dc.titleStochastic Perturbation Theoryen_US
dc.typeTechnical Reporten_US

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