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| Documentation |[](https://juliadiff.org/DifferentiationInterface.jl/DifferentiationInterface/stable/)[](https://juliadiff.org/DifferentiationInterface.jl/DifferentiationInterface/dev/)[](https://github.com/SciML/ColPrac)|
3. if you use sparse differentiation capabilities, our companion packages [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) and [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) which provide these capabilities (see their respective repositories for guidelines).
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3. if you use sparse differentiation capabilities, our companion packages [SparseConnectivityTracer.jl](https://github.com/adrhill/SparseConnectivityTracer.jl) and [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl) which provide these capabilities (see their respective repositories for guidelines).
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For the first two items, you can use the provided [`CITATION.cff`](https://github.com/JuliaDiff/DifferentiationInterface.jl/blob/main/CITATION.cff) file or the following BibTeX entries:
Copy file name to clipboardExpand all lines: DifferentiationInterface/docs/src/explanation/advanced.md
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@@ -23,7 +23,7 @@ An `AutoSparse` backend must be constructed from three ingredients:
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+[`DenseSparsityDetector`](@ref) from DifferentiationInterface.jl (beware that this detector only gives a locally valid pattern)
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+[`KnownJacobianSparsityDetector`](@extref ADTypes.KnownJacobianSparsityDetector) or [`KnownHessianSparsityDetector`](@extref ADTypes.KnownHessianSparsityDetector) from [ADTypes.jl](https://github.com/SciML/ADTypes.jl) (if you already know the pattern)
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3. A coloring algorithm following the [`ADTypes.AbstractColoringAlgorithm`](@extref ADTypes.AbstractColoringAlgorithm) interface, such as those from [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl):
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3. A coloring algorithm following the [`ADTypes.AbstractColoringAlgorithm`](@extref ADTypes.AbstractColoringAlgorithm) interface, such as those from [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl):
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+[`GreedyColoringAlgorithm`](@extref SparseMatrixColorings.GreedyColoringAlgorithm) (our generic recommendation, don't forget to tune the `order` parameter)
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+[`ConstantColoringAlgorithm`](@extref SparseMatrixColorings.ConstantColoringAlgorithm) (if you have already computed the optimal coloring and always want to return it)
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The complexity of sparse Jacobians or Hessians grows with the number of distinct colors in a coloring of the sparsity pattern.
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To reduce this number of colors, [`GreedyColoringAlgorithm`](@extref SparseMatrixColorings.GreedyColoringAlgorithm) has two main settings: the order used for vertices and the decompression method.
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Depending on your use case, you may want to modify either of these options to increase performance.
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See the documentation of [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) for details.
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See the documentation of [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl) for details.
Copy file name to clipboardExpand all lines: DifferentiationInterface/docs/src/tutorials/advanced.md
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Sparse preparation is more costly than dense preparation, but it is even more essential.
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Indeed, once preparation is done, sparse differentiation is much faster than dense differentiation, because it makes fewer calls to the underlying function.
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Some result analysis functions from [SparseMatrixColorings.jl](https://github.com/gdalle/SparseMatrixColorings.jl) can help you figure out what the preparation contains.
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Some result analysis functions from [SparseMatrixColorings.jl](https://github.com/JuliaDiff/SparseMatrixColorings.jl) can help you figure out what the preparation contains.
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First, it records the sparsity pattern itself (the one returned by the detector).
[](https://github.com/SciML/ColPrac)
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| Documentation |[](https://juliadiff.org/DifferentiationInterface.jl/DifferentiationInterfaceTest/stable/)[](https://juliadiff.org/DifferentiationInterface.jl/DifferentiationInterfaceTest/dev/)[](https://github.com/SciML/ColPrac)|
Testing and benchmarking utilities for automatic differentiation (AD) in Julia, based on [DifferentiationInterface](https://github.com/JuliaDiff/DifferentiationInterface.jl/tree/main/DifferentiationInterface).
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## Goal
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Make it easy to know, for a given function:
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- which AD backends can differentiate it
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- how fast they can do it
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- which AD backends can differentiate it
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- how fast they can do it
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## Features
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- Definition of custom test scenarios
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- Correctness tests
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- Type stability tests
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- Count calls to the function
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- Benchmark runtime and allocations
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- Definition of custom test scenarios
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- Correctness tests
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- Type stability tests
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- Count calls to the function
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- Benchmark runtime and allocations
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## Installation
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subdir="DifferentiationInterfaceTest",
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)
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```
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## Citation
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See the citation instructions for [DifferentiationInterface](https://github.com/JuliaDiff/DifferentiationInterface.jl/tree/main/DifferentiationInterface).
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