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docs/src/gradients/gradients.md

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@@ -13,6 +13,8 @@ In some cases, function modifiers can even be composed on top of each other to c
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> In `Nonconvex`, function modifiers modify the behaviour of a function when differentiated once or twice using either `ForwardDiff` or any [`ChainRules`](https://github.com/JuliaDiff/ChainRules.jl)-compatible AD package, such as `Zygote.jl`. The following features are all implemented in [`NonconvexUtils.jl`](https://github.com/JuliaNonconvex/NonconvexUtils.jl) and re-exported from `Nonconvex`.
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## Table of contents
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```@contents
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Pages = ["user_defined.md", "other_ad.md", "chainrules_fd.md", "sparse.md", "symbolic.md", "implicit.md", "history.md"]
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Depth = 3

docs/src/problem/problem.md

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2. `DictModel` which assumes each variable has a name. The decision variables are stored in an `OrderedDict`, an ordered dictionary data structure.
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3. Start from `JuMP.Model` and convert it to `DictModel`. This is convenient to make use of `JuMP`'s user-friendly macros for variable and linear expression, objective or constraint definitions.
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## Table of contents
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```@contents
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Pages = ["model.md", "dict_model.md", "queries.md"]
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