Add a new claim

Author: ChrisRackauckas

small_grants.md

@@ -206,8 +206,38 @@ would be helpful for debugging.
 
 **Reviewers**: Chris Rackauckas
 
+## Refactor NonlinearSolve.jl and BoundaryValueDiffEq.jl to use Sub-Packages of Solvers (\$300 each)
+
+With the successful splitting of [OrdinaryDiffEq.jl](https://sciml.ai/news/2024/08/10/sciml_small_grants_successes/),
+we suspect that similar installation and loading time improvements can be had by
+splitting NonlinearSolve.jl and BoundaryValueDiffEq.jl in such a way that the solvers
+can precompile in parallel and allow for depending on only a portion of the algorithms.
+In particular, OrdinaryDiffEq.jl only needs to depend on a trust region method, meaning
+that other sets of methods can be fully discarded from its dependency stack.
+
+**Information to Get Started**: The OrdinaryDiffEq.jl solvers are all found in
+[the Github repository](https://github.com/SciML/OrdinaryDiffEq.jl) and
+the format of the package is docmented in the
+[developer documentation](https://docs.sciml.ai/DiffEqDevDocs/stable/). [https://github.com/SciML/OrdinaryDiffEq.jl/issues/2177](https://github.com/SciML/OrdinaryDiffEq.jl/issues/2177)
+documents the process on OrdinaryDiffEq.jl to 
+
+**Related Issues**: 
+
+**Success Criteria**: The independent solver packages are registered and released,
+and a breaking update to OrdinaryDiffEq.jl is released which reduces the loading
+time by not including all solvers by default. This success also requires updating
+package documentation to reflect these changes.
+
+**Recommended Skills**: Since all of the code for the solvers exists and this a refactor,
+no prior knowledge of numerical differential equations is required. Only standard software
+development skills and test-driven development of a large code base is required.
+
+**Reviewers**: Chris Rackauckas, Avik Pal
+
 ## Refactor OrdinaryDiffEq.jl Solver Sets to Reuse perform_step! Implementations via Tableaus (\$100/solver set)
 
+**In Progress**: Claimed by Param Umesh Thakkar for the time period of August 11th, 2024 - September 11th 2024. 
+
 The perform_step! implementations per solver in OrdinaryDiffEq.jl are often "bespoke", i.e.
 one step implementation per solver. The reason is because the package code grew organically
 over time and this is the easiest way to ensure performance and write out a new method.