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Specialize functions on f parameter for trim compatibility #686

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Merged
merged 1 commit into from
Aug 21, 2025
Merged

Specialize functions on f parameter for trim compatibility #686

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4 changes: 2 additions & 2 deletions lib/NonlinearSolveHomotopyContinuation/src/jacobian_handling.jl
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Expand Up @@ -111,7 +111,7 @@ and `p` the parameter object.

The returned function must have the signature required by `HomotopySystemWrapper`.
"""
function construct_jacobian(f, autodiff, variant, u0, p)
function construct_jacobian(f::F, autodiff, variant, u0, p) where F
if variant == Scalar
tmp = reinterpret(Float64, Vector{ComplexF64}(undef, 1))
else
Expand Down Expand Up @@ -182,7 +182,7 @@ end

Construct an `EnzymeJacobian` function.
"""
function construct_jacobian(f, autodiff::AutoEnzyme, variant, u0, p)
function construct_jacobian(f::F, autodiff::AutoEnzyme, variant, u0, p) where F
if variant == Scalar
prep = DI.prepare_derivative(f, autodiff, u0, DI.Constant(p), strict = Val(false))
else
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4 changes: 2 additions & 2 deletions lib/NonlinearSolveSciPy/src/NonlinearSolveSciPy.jl
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Expand Up @@ -94,7 +94,7 @@ end
"""
Internal: wrap a Julia residual function into a Python callable
"""
function _make_py_residual(f, p)
function _make_py_residual(f::F, p) where F
return pyfunc(x_py -> begin
x = Vector{Float64}(x_py)
r = f(x, p)
Expand All @@ -105,7 +105,7 @@ end
"""
Internal: wrap a Julia scalar function into a Python callable
"""
function _make_py_scalar(f, p)
function _make_py_scalar(f::F, p) where F
return pyfunc(x_py -> begin
x = Float64(x_py)
return f(x, p)
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2 changes: 1 addition & 1 deletion lib/SimpleNonlinearSolve/src/trust_region.jl
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Expand Up @@ -193,7 +193,7 @@ function SciMLBase.__solve(
return SciMLBase.build_solution(prob, alg, x, fx; retcode = ReturnCode.MaxIters)
end

function dogleg_method!!(cache, J, f, g, Δ)
function dogleg_method!!(cache, J, f::F, g, Δ) where F
(; δsd, δN_δsd, δN) = cache

# Compute the Newton step
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