Editing Automatic differentiation (section)

==External links==
* [http://www.autodiff.org/ www.autodiff.org], An "entry site to everything you want to know about automatic differentiation"
* [http://www.autodiff.org/?module=Applications&application=HC1 Automatic Differentiation of Parallel OpenMP Programs]
* [https://www.researchgate.net/publication/241730000_Automatic_Differentiation_C_Templates_and_Photogrammetry Automatic Differentiation, C++ Templates and Photogrammetry]
* [https://web.archive.org/web/20070927120356/http://www.vivlabs.com/subpage_ad.php Automatic Differentiation, Operator Overloading Approach]
* [http://tapenade.inria.fr:8080/tapenade/index.jsp Compute analytic derivatives of any Fortran77, Fortran95, or C program through a web-based interface] Automatic Differentiation of Fortran programs
* [http://www.win-vector.com/dfiles/AutomaticDifferentiationWithScala.pdf Description and example code for forward Automatic Differentiation in Scala] {{Webarchive|url=https://web.archive.org/web/20160803214549/http://www.win-vector.com/dfiles/AutomaticDifferentiationWithScala.pdf |date=2016-08-03 }}
* [https://www.finmath.net/finmath-lib/concepts/stochasticautomaticdifferentiation/ finmath-lib stochastic automatic differentiation], Automatic differentiation for random variables (Java implementation of the stochastic automatic differentiation).
* [https://web.archive.org/web/20140423121504/http://developers.opengamma.com/quantitative-research/Adjoint-Algorithmic-Differentiation-OpenGamma.pdf Adjoint Algorithmic Differentiation: Calibration and Implicit Function Theorem]
* [http://www.quantandfinancial.com/2017/02/automatic-differentiation-templated.html C++ Template-based automatic differentiation article] and [https://github.com/omartinsky/QuantAndFinancial/tree/master/autodiff_templated implementation]
* [https://github.com/google/tangent Tangent] [https://research.googleblog.com/2017/11/tangent-source-to-source-debuggable.html Source-to-Source Debuggable Derivatives]
* [http://www.nag.co.uk/doc/techrep/pdf/tr5_10.pdf Exact First- and Second-Order Greeks by Algorithmic Differentiation]
* [http://www.nag.co.uk/Market/articles/adjoint-algorithmic-differentiation-of-gpu-accelerated-app.pdf Adjoint Algorithmic Differentiation of a GPU Accelerated Application]
* [http://www.nag.co.uk/Market/seminars/Uwe_AD_Slides_July13.pdf Adjoint Methods in Computational Finance Software Tool Support for Algorithmic Differentiationop]
* [https://www.intel.com/content/dam/www/public/us/en/documents/white-papers/xva-pricing-application-financial-services-white-papers.pdf More than a Thousand Fold Speed Up for xVA Pricing Calculations with Intel Xeon Scalable Processors]
* [https://github.com/ExcessPhase/ctaylor Sparse truncated Taylor series implementation with VBIC95 example for higher order derivatives]

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[[Category:Differential calculus]]
[[Category:Computer algebra]]
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