Comments (4)
Not certain if this package wants to support complex AD (with Jacobians as the primitive, complex support is a little awkward), but to me it seems like, if the AD supports complex numbers, then at least for pushforwards, pullbacks, and gradients, it'd be nice if we did too.
from abstractdifferentiation.jl.
I agree. Even Jacobians can be supported as long as either the inputs or outputs are real.
from abstractdifferentiation.jl.
That's right, and even with both complex inputs and outputs, it can be supported by interleaving the real and imaginary parts into one real vector, but having the eltype be dependent on the real-ness of input and output starts to feel messy.
from abstractdifferentiation.jl.
ya complex input and output is messy, let's stick to the simple cases
from abstractdifferentiation.jl.
Related Issues (20)
- `AD.jacobian` much slower than `Zygote.jacobian`
- JET.jl reports possible errors on `AD.gradient` that do not appear with `Zygote.gradient` HOT 3
- Code inside function with rrule should not run HOT 2
- Using AbstractDifferentiation and Zygote as package dependencies errors: `UndefVarError: ZygoteBackend not defined`
- Jacobians for functions beyond vector-to-vector?
- Whats the reason for the derivative / gradient difference?
- AD failure where Zygote succeeds HOT 3
- ForwardDiff is broken for differently sized inputs
- API for user code to detect if it's being differentiated HOT 11
- Feature request: option to turn off ForwardDiff tagging
- Zygote context cache incorrectly(?) persists between AD calls
- Missing `value_derivative_and_second_derivative`?
- Replace Requires.jl with conditional dependencies for Julia 1.9 HOT 2
- LoadError: UndefVarError: StaticArrays not defined HOT 2
- add `ForwardRuleConfigBackend` (to match `ReverseRuleConfigBackend`) HOT 7
- Lazy Jacobian multiplication HOT 6
- ERROR: MethodError: no method matching ZygoteBackend() HOT 1
- `lazy_jacobian` is less generic than `pullback_function`
- Enzyme support HOT 2
- No method matching ForwardDiffBackend/ZygoteBackend. v1.9 HOT 2
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from abstractdifferentiation.jl.