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autodiff will be failed to compile when using `float` in `dual` mode.
Below is a simple example:
```c++
#include
#include
int main(int argc, char *argv[]) {
Eigen::Vector2f x;
x.set…
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Hi @JamesYang007!
This repo seems pretty nice, especially your philosophy as laid out in [your paper](https://arxiv.org/abs/2102.03681) of arguing the benefit of having a pair of pointers of matric…
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This was found while writing tests for #1307, where the function below is composed with `cholesky`:
```julia
square(A) = A * adjoint(A)
A = rand(ComplexF64, 5, 5)
ishermitian(square(A)) # true
…
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Hello,
I saw that there is a documented `hessian` driver for the reverse mode. And as I checked through the forward eigen header I found the counterpart. However, I cannot get it to work. My exampl…
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There are a bunch of autodiff functors that are implemented in Stan but not exposed yet in BridgeStan. The two most basic are already done. Most of them other than directional derivatives require f…
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Hi!
This is a follow-up on #181. The use case is to evaluate the derivatives (e.g. gradient, hessian) of some loss function $\mathcal{L}$ with respect to some variable $\theta$ at the gradient equi…
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Click to expand!
### Issue Type
Bug
### Source
binary
### Tensorflow Version
tf 2.9
### Custom Code
Yes
### OS Platform and Distribution
Linux Ubuntu 20.04
### Mobile device
_No respons…
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I've run into an issue where I want to compute the gradient of an implicit function that itself depends on another implicit function. I can do the operation successfully with `FowardDiff`, however I …
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### Issue type
Bug
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf 2.14.0
### Custom code
Yes
### OS platform and distribution
window…
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#### Summary:
Add testing for higher order autodiff. We need to be able to determine where we aren't able to compile models in higher order.
This goes along with stan-dev/math#403.
#### Descrip…