Specialize functions on f parameter for trim compatibility #686
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Similar to SciML#684 and SciML/OrdinaryDiffEq.jl#2854, this PR adds type specialization for the f parameter in several functions across NonlinearSolve.jl to improve compatibility with --trim and reduce dynamic dispatch. Functions specialized: - _make_py_residual in NonlinearSolveSciPy.jl - _make_py_scalar in NonlinearSolveSciPy.jl - dogleg_method!! in trust_region.jl - construct_jacobian in jacobian_handling.jl (both general and AutoEnzyme versions) These functions either call f directly, pass f to other functions, or work with f in ways that benefit from type specialization for better compiler optimizations.
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Summary
Similar to #684 and SciML/OrdinaryDiffEq.jl#2854, this PR adds type specialization for the
fparameter in several functions across NonlinearSolve.jl to improve compatibility with--trimand reduce dynamic dispatch.Functions specialized
_make_py_residualinlib/NonlinearSolveSciPy/src/NonlinearSolveSciPy.jl:97_make_py_scalarinlib/NonlinearSolveSciPy/src/NonlinearSolveSciPy.jl:108dogleg_method!!inlib/SimpleNonlinearSolve/src/trust_region.jl:196construct_jacobianinlib/NonlinearSolveHomotopyContinuation/src/jacobian_handling.jl:114(general version)construct_jacobianinlib/NonlinearSolveHomotopyContinuation/src/jacobian_handling.jl:185(AutoEnzyme version)Rationale
These functions either:
fdirectly as a functionfto other functions (like ComplexJacobianWrapper)fin ways that benefit from type specializationAdding
where Ftype parameters enables better compiler optimizations and reduces dynamic dispatch, following the same pattern established in PR #684.Testing
The package compiles and loads successfully with these changes.