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docs: remove NonlinearSolve refactoring
Already near completion in SciML/NonlinearSolve.jl#483
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small_grants.md

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**Reviewers**: Chris Rackauckas
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## Refactor NonlinearSolve.jl to use Sub-Packages of Solvers (\$300)
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With the successful splitting of [OrdinaryDiffEq.jl](https://sciml.ai/news/2024/08/10/sciml_small_grants_successes/),
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we suspect that similar installation and loading time improvements can be had by
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splitting NonlinearSolve.jl and BoundaryValueDiffEq.jl in such a way that the solvers
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can precompile in parallel and allow for depending on only a portion of the algorithms.
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In particular, OrdinaryDiffEq.jl only needs to depend on a trust region method, meaning
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that other sets of methods can be fully discarded from its dependency stack.
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**Information to Get Started**: The OrdinaryDiffEq.jl solvers are all found in
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[the Github repository](https://github.com/SciML/OrdinaryDiffEq.jl) and
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the format of the package is docmented in the
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[developer documentation](https://docs.sciml.ai/DiffEqDevDocs/stable/). [https://github.com/SciML/OrdinaryDiffEq.jl/issues/2177](https://github.com/SciML/OrdinaryDiffEq.jl/issues/2177)
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documents the process on OrdinaryDiffEq.jl to
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**Related Issues**:
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**Success Criteria**: The independent solver packages are registered and released,
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and a breaking update to OrdinaryDiffEq.jl is released which reduces the loading
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time by not including all solvers by default. This success also requires updating
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package documentation to reflect these changes.
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**Recommended Skills**: Since all of the code for the solvers exists and this a refactor,
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no prior knowledge of numerical differential equations is required. Only standard software
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development skills and test-driven development of a large code base is required.
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**Reviewers**: Chris Rackauckas, Avik Pal
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## Refactor OrdinaryDiffEq.jl Solver Sets to Reuse perform_step! Implementations via Tableaus (\$100/solver set)
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**In Progress**: Claimed by Param Umesh Thakkar for the time period of August 11th, 2024 - September 11th 2024.

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