Titlebook: Automatic Differentiation of Algorithms; From Simulation to O George Corliss,Christèle Faure,Uwe Naumann Book 2002 Springer Science+Busines

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Communication Skills in Medical Education Automatic differentiation enables, in a completely mechanical fashion, algorithmic changes by switching from a quasi-Newton method, where first order derivatives are approximated by finite differences, to a modified Gauss-Newton method using exact first order derivatives. Compared to the original c

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Advances in Science, Technology & Innovationiteratively. Investigated methods include automatic differentiation (AD) with the Odyssée software (forward and reverse modes) and manual differentiation (MD) using the model’s adjoint equations. The comparison mainly focuses on accuracy and computing efficiency, as well as on development effort. Wh

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Tabassum Zehra,Rukhsana Wamiq Zuberion constitutes an opportunity to achieve both higher run-time efficiency and an increased feasibility of higher-order uncertainty analysis of complex models. In this article we present an overview of the derivative requirements of nonlinear regression routines. We further describe our experience in

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Angelica Hüsser,Michael Schanneusing a direct transcription method. The resulting nonlinear programming problem is solved using the sequential quadratic programming algorithm SNOPT for constrained optimisation. The automatic differentiation software tool .. is used for the evaluation of the first-order derivatives of objective an

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https://doi.org/10.1007/978-3-030-66296-7m incurs amazingly low overheads: the cost (measured in target function evaluations) is independent of the number of discrete time-steps. The algorithm can be modified to verify that the Hessian contains no eigenvalues less than a postulated quantity, and to produce an appropriate descent direction