Литература#

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Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. Automatic differentiation in machine learning: a survey. Journal of Machine Learning Research, 18:1–43, 2018. URL: https://jmlr.org/papers/v18/17-468.html.

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J. Bezanson, A. Edelman, S. Karpinski, and V. B. Shah. Julia: a fresh approach to numerical computing. SIAM Review, 59(1):65–98, 2017. doi:10.1137/141000671.

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Andreas Griewank. Who invented the reverse mode of differentiation? In Martin Grötschel, editor, Optimization Stories, pages 389–400. EMS Press, 2012. doi:10.4171/dms/6/38.

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Michael Innes. Don't unroll adjoint: differentiating ssa-form programs. CoRR, 2018. doi:https://doi.org/10.48550/arXiv.1810.07951.

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David Johnston. Advances in thermodynamics of the van der waals fluid. arXiv, 2014. doi:10.1088/978-1-627-05532-1.

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Mykel J. Kochenderfer and Tim A. Wheeler. Algorithms for optimization. The MIT Press, 2019. ISBN 978-0-262-03942-0.

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[LeV02]

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[MSA03]

Joaquim R. R. A. Martins, Peter Sturdza, and Juan J. Alonso. The complex-step derivative approximation. ACM Transactions on Mathematical Software, 29(3):245–262, 2003. doi:10.1145/838250.838251.

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J. Revels, M. Lubin, and T. Papamarkou. Forward-mode automatic differentiation in Julia. arXiv:1607.07892 [cs.MS], 2016. URL: https://arxiv.org/abs/1607.07892.

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[02]

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