Comments (2)
See also JuliaDiff/ReverseDiff.jl#251
from forwarddiff.jl.
I can't manage to get an actual bug out of this, presumably because the StaticArrays extension of ForwardDiff overloads the vulnerable parts I've tried. But for ReverseDiff this does lead to wrong behavior
from forwarddiff.jl.
Related Issues (20)
- Cancellation with sparse arrays HOT 5
- Implement hessian! for scalar x
- Implement gammalogccdf for ForwardDiff HOT 1
- `ForwardDiff.jacobian` throws error for `fft` HOT 1
- Correctly forming nested dual numbers. HOT 8
- Derivative of a function of derivatives HOT 7
- Symbolics.jl compatibility HOT 1
- Support derivative(f, ::Complex) HOT 1
- `ForwardDiff` fails to compute correct derivative HOT 3
- Incorrect Hessian by `exp` function HOT 1
- Method ambiguities reported by Aqua HOT 3
- Document internals? HOT 1
- Bug (NaNs) when differentiating eigenvectors of Symmetric matrices
- Error requiring `Symbolics` from `Optimization` HOT 1
- promote_rule ambiguity with AbstractIrrational and ForwardDiff.Dual HOT 2
- Allocation tests broken since Julia 1.9
- LoadError: ArgumentError: Package AdaptStaticArraysCoreExt does not have Adapt in its dependencies: HOT 2
- Working with anonymous functions HOT 2
- `gradient!` allocates for matrices but not for vectors
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from forwarddiff.jl.