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• Q787371 subject Q6425293.
• Q787371 subject Q8376417.
• Q787371 abstract "In mathematics and computer algebra, automatic differentiation (AD), also called algorithmic differentiation or computational differentiation, is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.) and elementary functions (exp, log, sin, cos, etc.). By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program.Automatic differentiation is not: Symbolic differentiation, nor Numerical differentiation (the method of finite differences).These classical methods run into problems: symbolic differentiation leads to inefficient code (unless carefully done) and faces the difficulty of converting a computer program into a single expression, while numerical differentiation can introduce round-off errors in the discretization process and cancellation. Both classical methods have problems with calculating higher derivatives, where the complexity and errors increase. Finally, both classical methods are slow at computing the partial derivatives of a function with respect to many inputs, as is needed for gradient-based optimization algorithms. Automatic differentiation solves all of these problems, at the expense of introducing more software dependencies.".
• Q787371 thumbnail AutomaticDifferentiationNutshell.png?width=300.
• Q787371 wikiPageExternalLink trilinos.sandia.gov.
• Q787371 wikiPageExternalLink LICENSE-2.0.
• Q787371 wikiPageExternalLink cran.r-project.org.
• Q787371 wikiPageExternalLink SIAMRev74362.pdf.
• Q787371 wikiPageExternalLink admb-project.org.
• Q787371 wikiPageExternalLink autodiff.codeplex.com.
• Q787371 wikiPageExternalLink index_cosy.htm.
• Q787371 wikiPageExternalLink ceres-solver.org.
• Q787371 wikiPageExternalLink download?doi=10.1.1.89.7749&rep=rep1&type=pdf.
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• Q787371 wikiPageExternalLink theory.html.
• Q787371 wikiPageExternalLink theano.
• Q787371 wikiPageExternalLink Adjoint-Algorithmic-Differentiation-OpenGamma.pdf.
• Q787371 wikiPageExternalLink DiffSharp.
• Q787371 wikiPageExternalLink ScientificPython.
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• Q787371 wikiPageExternalLink funclib.codeplex.com.
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• Q787371 wikiPageExternalLink matlabad.com.
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• Q787371 wikiPageExternalLink www.casadi.org.
• Q787371 wikiPageExternalLink CppAD.
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• Q787371 wikiPageExternalLink www.fadbad.com.
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• Q787371 wikiPageExternalLink OpenAD.
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• Q787371 wikiPageExternalLink Uwe_AD_Slides_July13.pdf.
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• Q787371 wikiPageExternalLink AutomaticDifferentiationWithScala.pdf.
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• Q787371 wikiPageExternalLink ctaylor.codeplex.com.
• Q787371 wikiPageExternalLink latest.
• Q787371 wikiPageExternalLink DualNumbers.jl.
• Q787371 wikiPageExternalLink ForwardDiff.jl.
• Q787371 wikiPageExternalLink HyperDualNumbers.jl.
• Q787371 wikiPageExternalLink ReverseDiffSource.jl.
• Q787371 wikiPageExternalLink TaylorSeries.jl.
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• Q787371 wikiPageExternalLink CppADCodeGen.
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• Q787371 wikiPageExternalLink ReverseAD.
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• Q787371 wikiPageExternalLink golang.org.
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• Q787371 wikiPageWikiLink Q191290.
• Q787371 wikiPageWikiLink Q192897.

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