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Support specializing on functions #615

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merged 3 commits into from
Dec 14, 2022

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j-fu
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@j-fu j-fu commented Dec 8, 2022

See Performance Tips .

In many places this was not implemented. This in particular would fix #516.

@j-fu j-fu mentioned this pull request Dec 8, 2022
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codecov-commenter commented Dec 8, 2022

Codecov Report

Base: 87.29% // Head: 87.28% // Decreases project coverage by -0.01% ⚠️

Coverage data is based on head (3dbf735) compared to base (76335e6).
Patch coverage: 96.55% of modified lines in pull request are covered.

Additional details and impacted files
@@            Coverage Diff             @@
##           master     #615      +/-   ##
==========================================
- Coverage   87.29%   87.28%   -0.02%     
==========================================
  Files           9        9              
  Lines         913      912       -1     
==========================================
- Hits          797      796       -1     
  Misses        116      116              
Impacted Files Coverage Δ
src/hessian.jl 97.56% <90.90%> (-0.06%) ⬇️
src/derivative.jl 94.87% <100.00%> (ø)
src/gradient.jl 98.92% <100.00%> (ø)
src/jacobian.jl 99.33% <100.00%> (ø)

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@KristofferC KristofferC merged commit 6a19554 into JuliaDiff:master Dec 14, 2022
j-fu added a commit to WIAS-PDELib/VoronoiFVM.jl that referenced this pull request Feb 25, 2023
after specializing on function paramerter for jacobian! has been fixed
in JuliaDiff/ForwardDiff.jl#615
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KnutAM commented Feb 15, 2024

I'm not sure what the release plan for v0.11 is, but if not coming out soon, is backporting this fix to 0.10 desired?
(I can re-implement it on that branch if desired, I don't know if there is an easier way to do the backport)

devmotion pushed a commit that referenced this pull request Sep 30, 2024
* specialize on function in jacobian

* specilize on function parameters for derivative, gradient, hessian
devmotion pushed a commit that referenced this pull request Sep 30, 2024
* specialize on function in jacobian

* specilize on function parameters for derivative, gradient, hessian
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type instabilities when passing a function to jacobian!
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