Specialize on internalnorm function. #684
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This would perhaps have been caught by SciML#665 but we're waiting on 1.12-rc2 and SciML/SciMLBase.jl#1074 to land before this would be automatically checked.
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we should probably do this everywehere then |
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Actually it looks like most cases of |
Apply consistent `internalnorm::IN` templating to PolyAlgorithm and GeneralizedFirstOrder __init functions to prevent trimming errors. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
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Looks like it's just one other location. Fixed now. |
ChrisRackauckas-Claude
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Aug 21, 2025
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.
ChrisRackauckas
pushed a commit
that referenced
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Aug 21, 2025
Similar to #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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See JuliaLang/julia#59280 (comment)
and JuliaLang/julia#59215 (comment).