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• W2982219699 abstract "Abstract Algorithmic differentiation (AD), also known as automatic differentiation, is a technology for accurate and efficient evaluation of derivatives of a function given as a computer model. The evaluations of such models are essential building blocks in numerous scientific computing and data analysis applications, including optimization, parameter identification, sensitivity analysis, uncertainty quantification, nonlinear equation solving, and integration of differential equations. We provide an introduction to AD and present its basic ideas and techniques, some of its most important results, the implementation paradigms it relies on, the connection it has to other domains including machine learning and parallel computing, and a few of the major open problems in the area. Topics we discuss include: forward mode and reverse mode of AD, higher‐order derivatives, operator overloading and source transformation, sparsity exploitation, checkpointing, cross‐country mode, and differentiating iterative processes. This article is categorized under: Algorithmic Development > Scalable Statistical Methods Technologies > Data Preprocessing" @default.
• W2982219699 created "2019-11-01" @default.
• W2982219699 creator A5049685547 @default.
• W2982219699 creator A5076366075 @default.
• W2982219699 date "2019-10-24" @default.
• W2982219699 modified "2023-10-09" @default.
• W2982219699 title "An introduction to algorithmic differentiation" @default.
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• W2982219699 doi "https://doi.org/10.1002/widm.1334" @default.
• W2982219699 hasPublicationYear "2019" @default.
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