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Improve NLP documentation about ForwardDiff #2000

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Improve NLP documentation about ForwardDiff
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odow Jun 26, 2019
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16 changes: 13 additions & 3 deletions docs/src/nlp.md
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Expand Up @@ -193,9 +193,19 @@ JuMP uses [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) to
perform automatic differentiation; see the ForwardDiff.jl
[documentation](http://www.juliadiff.org/ForwardDiff.jl/v0.10.2/user/limitations.html)
for a description of how to write a function suitable for automatic
differentiation. The general guideline is to write code that is generic with
respect to the number type; don't assume that the input to the function is
`Float64`. To register a user-defined function with derivatives computed by
differentiation.

!!! note
The general guideline for writing ForwardDiff-compatible code is is to write
code that is generic with respect to the number type; don't assume that the
input to the function is `Float64`.
```julia
f(x::Float64) = 2 * x # Don't do this.
f(x::Real) = 2 * x # This is good.
f(x) = 2 * x # This is also good.
```

To register a user-defined function with derivatives computed by
automatic differentiation, use the `register` method as in the following
example:

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