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Copy file name to clipboardExpand all lines: DifferentiationInterface/docs/src/tutorial.md
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@@ -148,13 +148,14 @@ To compute sparse Jacobians or Hessians, you need three ingredients (read [this
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-[`TracerSparsityDetector`](@extref SparseConnectivityTracer.TracerSparsityDetector), implemented by [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) (our default recommendation)
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-[`SymbolicsSparsityDetector`](@ref DifferentiationInterface.SymbolicsSparsityDetector), implemented by DifferentiationInterface.jl with [Symbolics.jl](https://github.com/JuliaSymbolics/Symbolics.jl) but not exported nor part of the public API (it will soon be [transferred](https://github.com/JuliaSymbolics/Symbolics.jl/pull/1134))
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3. A coloring algorithm like:
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-[`GreedyColoringAlgorithm`](@ref), implemented by DifferentiationInterface.jl
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-`GreedyColoringAlgorithm`, implemented by [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) and re-exported by DifferentiationInterface.jl
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ADTypes.jl v1.0 provides the [`AutoSparse`](@extref ADTypes.AutoSparse) wrapper to combine these three ingredients, and DifferentiationInterface.jl re-exports it.
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Here's an example:
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```@example tuto
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using SparseConnectivityTracer
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using SparseConnectivityTracer: TracerSparsityDetector
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using SparseMatrixColorings: GreedyColoringAlgorithm
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