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dc.contributor.authorColeman, Thomas F.en_US
dc.contributor.authorLi, Yuyingen_US
dc.date.accessioned2007-04-23T16:32:46Z
dc.date.available2007-04-23T16:32:46Z
dc.date.issued1992-11en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR92-1314en_US
dc.identifier.urihttps://hdl.handle.net/1813/6164
dc.description.abstractWe consider a new algorithm, a reflective Newton method, for the problem of minimizing a smooth nonlinear function of many variables, subject to upper and/or lower bounds on some of the variables. This approach generates strictly feasible iterates by following piecewise linear paths ("reflection" paths) to generate improved iterates. The reflective Newton approach does not require identification of an "activity set". In this report we establish that the reflective Newton approach is globally and quadratically convergent. Moreover, we develop a specific example of this general reflective path approach suitable for large-scale and sparse problems.en_US
dc.format.extent3090911 bytes
dc.format.extent642198 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleOn The Convergence of Reflective Newton Methods for Large-Scale Nonlinear Minimization Subject to Boundsen_US
dc.typetechnical reporten_US


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