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dc.contributor.authorBartholomew-Biggs, M.
dc.contributor.authorBartholomew-Biggs, Lynda
dc.contributor.authorChristianson, B.
dc.date.accessioned2015-03-02T10:18:29Z
dc.date.available2015-03-02T10:18:29Z
dc.date.issued1994
dc.identifier.citationBartholomew-Biggs , M , Bartholomew-Biggs , L & Christianson , B 1994 , ' Optimization and automatic differentiation in Ada : some practical experience ' , Optimization Methods and Software , vol. 4 , no. 1 , pp. 47-73 . https://doi.org/10.1080/10556789408805577
dc.identifier.issn1055-6788
dc.identifier.otherPURE: 8160770
dc.identifier.otherPURE UUID: 53f1bf40-5556-4abb-b1be-de78ad9c217e
dc.identifier.otherScopus: 0028712396
dc.identifier.urihttp://hdl.handle.net/2299/15491
dc.description.abstractThis paper describes an investigation into the performance of three Ada packages for automatic differentiation. Two of these implement the forward accumulation approach while the third employs reverse accumulation. Each package is used to provide gradient information required by a number of optimization calculations, including examples of unconstrained, constrained and least-squares problems. The results show how automatic differentiation methods can be influenced in practice by the size, complexity and sparsity of a problem. They also demonstrate ways in which the methods should interface with different types of optimization procedure. Finally, and perhaps most significantly, the results show how the performance of automatic differentiation codes can depend on hardware and system software considerations that are sometimes ignored by numerical mathematicians. A subsidiary aim of this paper is to provide a “shop window” for user friendly forms of automatic differentiation. The underlying mathematical ideas have been quite widely discussed in the literature: but their implementation and use seems to have been perceived as too difficult for the non-specialist. The examples in this paper are intended to demonstrate that this need not be the caseen
dc.format.extent27
dc.language.isoeng
dc.relation.ispartofOptimization Methods and Software
dc.titleOptimization and automatic differentiation in Ada : some practical experienceen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted1994
rioxxterms.versionofrecordhttps://doi.org/10.1080/10556789408805577
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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