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dc.contributor.authorBartholomew-Biggs, Michael
dc.contributor.authorBeddiaf, Salah
dc.contributor.authorChristianson, Bruce
dc.date.accessioned2022-09-08T15:00:03Z
dc.date.available2022-09-08T15:00:03Z
dc.date.issued2022-08-26
dc.identifier.citationBartholomew-Biggs , M , Beddiaf , S & Christianson , B 2022 , ' Global Convergence of a Curvilinear Search for Non-Convex Optimization ' , Numerical Algorithms . https://doi.org/10.1007/s11075-022-01375-y
dc.identifier.issn1017-1398
dc.identifier.otherPURE: 27813791
dc.identifier.otherPURE UUID: a97df5e4-c1dc-4389-8078-dbe3797b72ed
dc.identifier.otherScopus: 85137063467
dc.identifier.urihttp://hdl.handle.net/2299/25753
dc.description© 2022 Springer. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1007/s11075-022-01375-y
dc.description.abstractFor a non-convex function f : R^n → R with gradient g and Hessian H, define a step vector p(μ,x) as a function of scalar parameter μ and position vector x by the equation (H(x) + μI)p(μ, x) = −g(x). Under mild conditions on f, we construct criteria for selecting μ so as to ensure that the algorithm x := x + p(μ, x) descends to a second order stationary point of f, and avoids saddle points.en
dc.language.isoeng
dc.relation.ispartofNumerical Algorithms
dc.subjectNonlinear optimization; Newton-like methods; Non-convex functions
dc.subjectMathematics(all)
dc.titleGlobal Convergence of a Curvilinear Search for Non-Convex Optimizationen
dc.contributor.institutionDepartment of Physics, Astronomy and Mathematics
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionMathematical and Theoretical Physics
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.description.statusPeer reviewed
dc.date.embargoedUntil2023-08-26
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1007/s11075-022-01375-y
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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