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This article provides an overview of some of the mathematical principles of Automatic Differentiation (AD).
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B. A. Pearlmutter and J. M. Siskind, Reverse-Mode AD in a Functional Framework: Lambda the Ultimate Backpropagator, TOPLAS 30 (2) (2008), 1–36
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J. M. Siskind and B. A. Pearlmutter, Nesting Forward-Mode AD in a Functional Framework, Higher-Order and Symbolic Computation 21 (4) (2008),361–376
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A. Griewank, A Mathematical View of Automatic Differentiation, Acta Numerica 12 (2003), 321–398
2003
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B. A. Pearlmutter and J. M. Siskind, Lazy Multivariate Higher-Order Forward-Mode AD, Proc of the 2007 Symposium on Principles of Programming Languages, Nice, France, 2007 , 155–160
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A. Griewank and A. Walther, Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, second edition, SIAM, Philadelphia, PA, 2008
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W. K. Clifford, Preliminary Sketch of Bi-quaternions, Proceedings of the London Mathematical Society 4 (1873), 381–395
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O. Manzyuk, A Simply Typed λ \lambda -Calculus of Forward Automatic Differentiation, Electronic Notes in Theoretical Computer Science 286 (2012), 257–272
2012
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A. G. Baydin, B. A. Pearlmutter, DiffSharp: Automatic Differentiation Library, version of 17th June 2015, http://diffsharp.github.io/DiffSharp/
2015
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