Automatic Differentiation Tools in Optimization Software

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dc.creatorMoré, Jorge J.
dc.date2001-01-03
dc.date.accessioned2026-07-07T03:16:49Z
dc.date.available2026-07-07T03:16:49Z
dc.descriptionWe discuss the role of automatic differentiation tools in optimization software. We emphasize issues that are important to large-scale optimization and that have proved useful in the installation of nonlinear solvers in the NEOS Server. Our discussion centers on the computation of the gradient and Hessian matrix for partially separable functions and shows that the gradient and Hessian matrix can be computed with guaranteed bounds in time and memory requirements
dc.description11 pages
dc.identifierhttps://arxiv.org/abs/cs/0101001
dc.identifierhttp://arxiv.org/abs/cs/0101001
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30499
dc.subjectMathematical Software
dc.subjectG.1.6
dc.titleAutomatic Differentiation Tools in Optimization Software
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