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Update 0_intro.jl (#117)
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examples/0_intro.jl

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@@ -85,7 +85,7 @@ We represent it using a type called `ImplicitFunction`, which you will see in ac
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First we define a forward mapping correponding to the function we consider.
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First we define a forward mapping corresponding to the function we consider.
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It returns the actual output $y(x)$ of the function, and can be thought of as a black box solver.
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Importantly, this Julia callable _doesn't need to be differentiable by automatic differentiation packages but the underlying function still needs to be mathematically differentiable_.
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@test implicit(x) sqrt.(x) #src
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And when we try to compute its Jacobian, the [implicit function theorem](https://en.wikipedia.org/wiki/Implicit_function_theorem) is applied in the background to circumvent the lack of differentiablility of the forward mapping.
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And when we try to compute its Jacobian, the [implicit function theorem](https://en.wikipedia.org/wiki/Implicit_function_theorem) is applied in the background to circumvent the lack of differentiability of the forward mapping.
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# ## Forward and reverse mode autodiff

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