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Separable least squares, variable projection and the Gauss-Newton algorithm

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dc.contributor.authorOsborne, Michael
dc.date.accessioned2015-12-08T22:39:02Z
dc.date.issued2007
dc.date.updated2016-02-24T09:52:39Z
dc.description.abstractA regression problem is separable if the model can be represented as a linear combination of functions which have a nonlinear parametric dependence. The Gauss-Newton algorithm is a method for minimizing the residual sum of squares in such problems. It is
dc.identifier.issn1068-9613
dc.identifier.urihttp://hdl.handle.net/1885/36072
dc.publisherKent State University
dc.sourceElectronic Transactions on Numerical Analysis
dc.subjectKeywords: Algorithms; Boolean functions; Convergence of numerical methods; Curve fitting; Errors; Maximum likelihood estimation; Measurement errors; Mobile telecommunication systems; Newton-Raphson method; Random errors; Consistency; Expected Hessian; Kaufman's mod Consistency; Expected Hessian; Kaufman's modification; Large data sets; Law of large numbers; Maximum likelihood; Newton's method; Nonlinear least; Random errors; Rate of convergence; Scoring; Squares
dc.titleSeparable least squares, variable projection and the Gauss-Newton algorithm
dc.typeJournal article
local.bibliographicCitation.lastpage15
local.bibliographicCitation.startpage1
local.contributor.affiliationOsborne, Michael, College of Physical and Mathematical Sciences, ANU
local.contributor.authoruidOsborne, Michael, u4592503
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor010301 - Numerical Analysis
local.identifier.ariespublicationu3169606xPUB132
local.identifier.citationvolume28
local.identifier.scopusID2-s2.0-54549121541
local.type.statusPublished Version

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